Diabetes mellitus is a major, and increasing, global problem. However, it has been shown that, through good management of blood glucose levels (BGLs), the associated and costly complications can be reduced significantly. In this pilot study, Elman recurrent artificial neural networks (ANNs) were used to make BGL predictions based on a history of BGLs, meal intake, and insulin injections. Twenty-eight datasets (from a single case scenario) were compiled from the freeware mathematical diabetes simulator, AIDA. It was found that the most accurate predictions were made during the nocturnal period of the 24 hour daily cycle. The accuracy of the nocturnal predictions was measured as the root mean square error over five test days (<svg style="vertical-align:-5.73166pt;width:68.4375px;" id="M1" height="18.5625" version="1.1" viewBox="0 0 68.4375 18.5625" width="68.4375" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,11.363)"><path id="x52" d="M631 18l-4 -26q-24 0 -35 1q-57 3 -92.5 23t-69.5 68q-21 30 -101 160q-13 22 -30.5 31t-54.5 9h-32v-159q0 -63 14 -77.5t74 -19.5v-28h-262v28q62 5 76 19.5t14 77.5v402q0 62 -13 76t-73 19v28h248q109 0 159 -34q67 -42 67 -131q0 -115 -127 -172q27 -52 89 -144&#xA;q49 -71 81 -104q31 -36 72 -47zM212 316h48q73 0 108 29q55 42 55 124q0 76 -42 112t-104 36q-41 0 -54 -10q-11 -7 -11 -44v-247z" /></g><g transform="matrix(.017,-0,0,-.017,10.619,11.363)"><path id="x4D" d="M861 0h-263v28q61 5 73 22t12 97l-4 408h-4l-244 -548h-20l-224 534h-2l-15 -285q-5 -98 -3 -166q1 -34 19.5 -45t72.5 -17v-28h-233v28q45 5 62.5 17t22.5 43q11 65 19 183l14 208q3 36 3.5 55t-2.5 37.5t-6.5 25t-16.5 13t-25.5 8t-39.5 4.5v28h164l228 -501l232 501&#xA;h170v-28q-68 -6 -81 -21.5t-11 -91.5l9 -362q2 -80 15 -97t78 -22v-28z" /></g><g transform="matrix(.017,-0,0,-.017,25.765,11.363)"><path id="x53" d="M409 504l-29 -5q-16 60 -44.5 96t-86.5 36q-54 0 -82.5 -32t-28.5 -77q0 -51 30.5 -82.5t96.5 -65.5l35.5 -18t33 -19.5t33.5 -23.5l27 -25.5t24.5 -32t13.5 -36.5t6 -45q0 -80 -62 -134.5t-160 -54.5q-47 0 -98 15q-22 7 -50 21q-8 23 -27 155l30 7q7 -27 18 -52&#xA;t30 -51.5t49 -42.5t67 -16q56 0 88 32.5t32 87.5q0 51 -31.5 82t-98.5 67q-80 44 -110 73q-55 53 -55 124q0 75 56 126.5t150 51.5q53 0 126 -23z" /></g><g transform="matrix(.017,-0,0,-.017,33.618,11.363)"><path id="x45" d="M517 162l29 -4q-18 -100 -35 -158h-482v28q68 5 83.5 20t15.5 77v398q0 66 -13 80t-73 19v28h446q0 -8 8 -144l-29 -4q-11 51 -32 85q-11 17 -35 23t-78 6h-76q-23 0 -28 -5t-5 -27v-228h94q67 0 83 12t25 63h29v-191h-29q-10 54 -26 65.5t-81 11.5h-95v-189&#xA;q0 -65 24 -81q19 -13 104 -13q57 0 83 7t43 27q28 33 50 94z" /></g><g transform="matrix(.012,-0,0,-.012,43.275,15.45)"><path id="x35" d="M153 550l-26 -186q79 31 111 31q90 0 141.5 -51t51.5 -119q0 -93 -89 -166q-85 -69 -173 -71q-32 0 -61.5 11.5t-41.5 23.5q-18 17 -17 34q2 16 22 33q14 9 26 -1q61 -50 124 -50q60 0 93 43.5t33 104.5q0 69 -41.5 110t-121.5 41q-53 0 -102 -20l38 305h286l6 -8&#xA;l-26 -65h-233z" /></g><g transform="matrix(.012,-0,0,-.012,50.962,15.45)"><path id="x64" d="M517 51v-25q-101 -18 -155 -38v65l-61 -36q-50 -29 -78 -29q-68 0 -126.5 58t-58.5 155q0 103 77 175.5t180 72.5q36 0 67 -12v143q0 48 -11.5 58.5t-65.5 14.5v25q79 9 156 34v-597q0 -38 7.5 -48t36.5 -13zM362 85v275q-35 51 -103 51q-20 0 -40.5 -8.5t-42 -28&#xA;t-35 -57.5t-13.5 -89q0 -83 42 -130t96 -47q51 0 96 34z" /></g><g transform="matrix(.012,-0,0,-.012,57.281,15.45)"><path id="x61" d="M433 39l-85 -51q-27 0 -47 20q-16 16 -24 44q-5 -3 -24 -16.5t-27 -18.5t-23 -13.5t-25 -12t-19 -3.5q-52 0 -86 36.5t-34 85.5q0 71 81 99q128 43 155 65v17q0 54 -23 83.5t-62 29.5q-28 0 -45 -19t-29 -66q-7 -23 -29 -23q-15 0 -29 13t-14 30t31 40q25 19 68.5 40.5&#xA;t81.5 29.5q49 0 82 -27q45 -39 45 -123v-185q0 -60 35 -60q18 0 36 11zM275 84v156q-12 -6 -49 -23t-41 -19q-31 -14 -46 -31t-15 -43q0 -35 22.5 -55.5t48.5 -20.5q19 0 42 10.5t38 25.5z" /></g><g transform="matrix(.012,-0,0,-.012,62.302,15.45)"><path id="x79" d="M478 437v-26q-39 -6 -53 -17.5t-35 -54.5q-12 -24 -26.5 -59.5l-44 -107.5l-55.5 -134q-86 -205 -103 -270q-6 -25 -32 -25q-21 0 -38.5 15t-17.5 33q0 19 25 37q40 24 75 88q18 34 31 69q7 20 -2 45q-67 191 -114 306q-17 43 -31.5 56.5t-54.5 18.5v26h202l1 -26&#xA;q-35 -5 -42 -16t4 -39l90 -250q57 137 92 249q10 28 0 39.5t-52 16.5v26h181z" /></g></svg>) not used during ANN training. For BGL predictions of up to 1 hour a <svg style="vertical-align:-5.73166pt;width:68.4375px;" id="M2" height="18.5625" version="1.1" viewBox="0 0 68.4375 18.5625" width="68.4375" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,11.363)"><use xlink:href="#x52" /></g><g transform="matrix(.017,-0,0,-.017,10.619,11.363)"><use xlink:href="#x4D" /></g><g transform="matrix(.017,-0,0,-.017,25.765,11.363)"><use xlink:href="#x53" /></g><g transform="matrix(.017,-0,0,-.017,33.618,11.363)"><use xlink:href="#x45" /></g><g transform="matrix(.012,-0,0,-.012,43.275,15.45)"><use xlink:href="#x35" /></g><g transform="matrix(.012,-0,0,-.012,50.962,15.45)"><use xlink:href="#x64" /></g><g transform="matrix(.012,-0,0,-.012,57.281,15.45)"><use xlink:href="#x61" /></g><g transform="matrix(.012,-0,0,-.012,62.302,15.45)"><use xlink:href="#x79" /></g></svg> of (±SD) <svg style="vertical-align:-0.3003pt;width:74.362503px;" id="M3" height="11.2875" version="1.1" viewBox="0 0 74.362503 11.2875" width="74.362503" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,10.862)"><path id="x30" d="M241 635q53 0 94 -28.5t63.5 -76t33.5 -102.5t11 -116q0 -58 -11 -112.5t-34 -103.5t-63.5 -78.5t-94.5 -29.5t-95 28t-64.5 75t-34.5 102.5t-11 118.5q0 58 11.5 112.5t34.5 103t64.5 78t95.5 29.5zM238 602q-32 0 -55.5 -25t-35.5 -68t-17.5 -91t-5.5 -105&#xA;q0 -76 10 -138.5t37 -107.5t69 -45q32 0 55.5 25t35.5 68.5t17.5 91.5t5.5 105t-5.5 105.5t-18 92t-36 68t-56.5 24.5z" /></g><g transform="matrix(.017,-0,0,-.017,8.222,10.862)"><path id="x2E" d="M113 -12q-24 0 -39.5 16t-15.5 42q0 24 16 40.5t40 16.5t40 -16.5t16 -40.5q0 -26 -16 -42t-41 -16z" /></g><g transform="matrix(.017,-0,0,-.017,12.098,10.862)"><path id="x31" d="M384 0h-275v27q67 5 81.5 18.5t14.5 68.5v385q0 38 -7.5 47.5t-40.5 10.5l-48 2v24q85 15 178 52v-521q0 -55 14.5 -68.5t82.5 -18.5v-27z" /></g><g transform="matrix(.017,-0,0,-.017,20.257,10.862)"><use xlink:href="#x35" /></g><g transform="matrix(.017,-0,0,-.017,32.19,10.862)"><path id="xB1" d="M535 290h-212v-206h-58v206h-213v50h213v193h58v-193h212v-50zM535 -22h-483v50h483v-50z" /></g><g transform="matrix(.017,-0,0,-.017,45.942,10.862)"><use xlink:href="#x30" /></g><g transform="matrix(.017,-0,0,-.017,54.102,10.862)"><use xlink:href="#x2E" /></g><g transform="matrix(.017,-0,0,-.017,57.977,10.862)"><use xlink:href="#x30" /></g><g transform="matrix(.017,-0,0,-.017,66.137,10.862)"><path id="x34" d="M456 178h-96v-72q0 -51 12.5 -62.5t72.5 -16.5v-27h-256v27q65 5 78 17t13 62v72h-260v28q182 271 300 426h40v-407h96v-47zM280 225v295h-2q-107 -148 -196 -295h198z" /></g></svg> mmol/L was observed. For BGL predictions up to 10 hours, a  
					<svg style="vertical-align:-5.73166pt;width:70.425003px;" id="M4" height="18.5625" version="1.1" viewBox="0 0 70.425003 18.5625" width="70.425003" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,11.363)"><use xlink:href="#x52" /></g><g transform="matrix(.017,-0,0,-.017,10.619,11.363)"><use xlink:href="#x4D" /></g><g transform="matrix(.017,-0,0,-.017,25.765,11.363)"><use xlink:href="#x53" /></g><g transform="matrix(.017,-0,0,-.017,33.618,11.363)"><use xlink:href="#x45" /></g><g transform="matrix(.012,-0,0,-.012,43.275,15.45)"><use xlink:href="#x35" /></g><g transform="matrix(.012,-0,0,-.012,52.95,15.45)"><use xlink:href="#x64" /></g><g transform="matrix(.012,-0,0,-.012,59.268,15.45)"><use xlink:href="#x61" /></g><g transform="matrix(.012,-0,0,-.012,64.29,15.45)"><use xlink:href="#x79" /></g></svg> of (±SD) <svg style="vertical-align:-0.3003pt;width:74.362503px;" id="M5" height="11.4" version="1.1" viewBox="0 0 74.362503 11.4" width="74.362503" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,10.963)"><use xlink:href="#x30" /></g><g transform="matrix(.017,-0,0,-.017,8.222,10.963)"><use xlink:href="#x2E" /></g><g transform="matrix(.017,-0,0,-.017,12.098,10.963)"><use xlink:href="#x31" /></g><g transform="matrix(.017,-0,0,-.017,20.257,10.963)"><use xlink:href="#x34" /></g><g transform="matrix(.017,-0,0,-.017,32.19,10.963)"><use xlink:href="#xB1" /></g><g transform="matrix(.017,-0,0,-.017,45.942,10.963)"><use xlink:href="#x30" /></g><g transform="matrix(.017,-0,0,-.017,54.102,10.963)"><use xlink:href="#x2E" /></g><g transform="matrix(.017,-0,0,-.017,57.977,10.963)"><use xlink:href="#x31" /></g><g transform="matrix(.017,-0,0,-.017,66.137,10.963)"><path id="x36" d="M137 343l67 33q37 17 63 17q79 0 129.5 -53t50.5 -131q0 -92 -58 -156.5t-147 -64.5t-147 68t-58 182q0 63 17 119t43 95.5t61.5 72t69 52t67.5 31.5q62 22 128 33l6 -32q-56 -11 -108 -35q-149 -71 -184 -231zM227 337q-47 0 -95 -27q-6 -23 -6 -70q0 -93 36 -155.5&#xA;t96 -62.5q53 0 78 45.5t25 105.5q0 68 -35 116t-99 48z" /></g></svg> mmol/L was observed. Future research will investigate a wider range of AIDA case scenarios, real-patient data, and data relating to other factors influencing BGLs. ANN paradigms based on real-time recurrent learning will also be explored to accommodate dynamic physiology in diabetes.

Gavin Robertson

Eldon D. Lehmann

William Sandham

David Hamilton

Blood Glucose Prediction Using Artificial Neural Networks Trained with the AIDA Diabetes Simulator: A Proof-of-Concept Pilot Study

Hierarchical interconnection networks (HINs) provide a framework for designing networks with reduced link cost by taking advantage of the locality of communication that exists in parallel applications. HINs employ multiple levels.  Lower-level networks provide local communication while higher-level networks facilitate remote communication. HINs provide fault tolerance in the presence of some faulty nodes and/or links. Existing HINs can be broadly classified into two classes. those that use nodes and/or links replication and those that use standby interface nodes. The first class includes Hierarchical Cubic Networks, Hierarchical Completely Connected Networks, and Triple-based Hierarchical Interconnection Networks. The second HINs class includes Modular Fault-Tolerant Hypercube Networks and Hierarchical Fault-Tolerant Interconnection Network. This paper presents a review and comparison of the topological properties of both classes of HINs. The topological properties considered are network degree, diameter, cost and packing density. The outcome of this study show among all HINs two networks that is, the Root-Folded Heawood (RFH) and the Flooded Heawood (FloH), belonging to the first HIN class provide the best network cost, defined as the product of network diameter and degree. The study also shows that HFCube<svg     style="vertical-align:-2.3205pt;width:35.662498px;" id="M1" height="15.0875" version="1.1" viewBox="0 0 35.662498 15.0875" width="35.662498"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
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</svg> provide the best packing density, that is, the smallest chip area required for VLSI implementation.

Mostafa Abd-El-Barr

Turki F. Al-Somani

Topological Properties of Hierarchical Interconnection Networks: A Review and Comparison

<i>Introduction</i>. AIDA is an interactive educational diabetes simulator available on the Internet without charge since 1996 (accessible at: http://www.2aida.org/). Since the program’s original release, users have developed new requirements, with new operating systems coming into use and more complex insulin management regimens being adopted. The current work has aimed to design a comprehensive diabetes simulation system from both a clinical and information technology perspective. <i>Methods</i>. A collaborative development is taking place with a new generic model of subcutaneous insulin absorption, permitting the simulation of rapidly-acting and very long-acting insulin analogues, as well as insulin injections larger than 40 units. This novel, physiological insulin absorption model has been incorporated into AIDA v4. Technical work has also been undertaken to install and operate the AIDA software within a DOSBox emulator, to ensure compatibility with Windows XP, Vista and 7 operating systems as well as Apple Macintosh computers running Parallels PC emulation software. <i>Results</i>. Plasma insulin simulations are demonstrated following subcutaneous injections of a rapidly-acting insulin analogue, a short-acting insulin preparation, intermediate-acting insulin, and a very long-acting insulin analogue for injected insulin doses up to 60 units of insulin. <i>Discussion.</i> The current work extends the useful life of the existing AIDA v4 program.

Cristina Tarín

Jorge Bondia

Edgar Teufel

Tibor Deutsch

Development of AIDA v4.3b Diabetes Simulator: Technical Upgrade to Support Incorporation of Lispro, Aspart, and Glargine Insulin Analogues

The prevalence of hidden node areas in IEEE 802.11 multihop MANETs continues to hinder the performance of routing protocols. This letter presents an analytical model that relates the hidden node area to the hop distance between two communicating nodes. Unlike descriptions from the literature, we describe the hidden node area in terms of multiple layers and the different levels of interference contributed by each layer. We then develop mathematical expressions to determine the probability of successful delivery and end-to-end delay of a packet transmitted over multiple hops to a receiver node exposed to hidden nodes, as a function of hop distance. The numerical results show that decreasing the hop distance increases the probability of successful packet reception at a receiver, at the cost of increased end-to-end delay. However, using a specified delay objective, routing protocols can institute a hop distance threshold metric to limit the number of transmissions that produce collisions in the hidden node area and, thus, maximize their performance.

Emeka Egbogah

Liqi Shi

Abraham Fapojuwo

Optimal Hidden Node Area for Enhancing Routing Protocol Performance in IEEE 802.11 Multihop MANETs

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Journal of Electrical and Computer Engineering publishes recent advances from the rapidly moving fields of both electrical engineering and computer engineering in the areas of circuits and systems, communications, power systems and signal processing.

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Journal of Electrical and Computer Engineering

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Journal of Electrical and Computer Engineering