Shortest Euclidean Distance Research Articles

A closed–form general solution for the distance of point-to-hyperbola in two dimensions

We consider the simple problem, in a two dimensional plane, the shortest Euclidean distance from a point to a hyperbola. Get rid of existing analytical or approximating methods from the literature, we give a closed-form general equation to this problem. Adhered in the end of the paper, the C++ and Python codes for the resolved general solution can facilitate practitioners in all fields to conduct the solution to their applications immediately.

Toward mechatronic MRI-guided focal laser ablation of the prostate: Robust registration for improved needle delivery.

Multiparametric MRI (mpMRI) is an effective tool for detecting and staging prostate cancer (PCa), guiding interventional therapy, and monitoring PCa treatment outcomes. MRI-guided focal laser ablation (FLA) therapy is an alternative, minimally invasive treatment method to conventional therapies, which has been demonstrated to control low-grade, localized PCa while preserving patient quality of life. The therapeutic success of FLA depends on the accurate placement of needles for adequate delivery of ablative energy to the target lesion. We previously developed an MR-compatible mechatronic system for prostate FLA needle guidance and validated its performance in open-air and clinical 3T in-bore experiments using virtualtargets. To develop a robust MRI-to-mechatronic system registration method and evaluate its in-bore MR-guided needle delivery accuracy in tissue-mimicking prostatephantoms. The improved registration multifiducial assembly houses thirty-six aqueous gadolinium-filled spheres distributed over a 7.3 × 7.3 × 5.2 cm volume. MRI-guided needle guidance accuracy was quantified in agar-based tissue-mimicking prostate phantoms on trajectories (N = 44) to virtual targets covering the mechatronic system's range of motion. 3T gradient-echo recalled (GRE) MRI images were acquired after needle insertions to each target, and the air-filled needle tracks were segmented. Needle guidance error was measured as the shortest Euclidean distance between the target point and the segmented needle trajectory, and angular error was measured as the angle between the targeted trajectory and the segmented needle trajectory. These measurements were made using both the previously designed four-sphere registration fiducial assembly on trajectories (N = 7) and compared with the improved multifiducial assembly using a Mann-Whitney Utest. The median needle guidance error of the system using the improved registration fiducial assembly at a depth of 10 cm was 1.02 mm with an interquartile range (IQR) of 0.42-2.94 mm. The upper limit of the one-sided 95% prediction interval of needle guidance error was 4.13 mm. The median (IQR) angular error was 0.0097 rad (0.0057-0.015rad) with a one-sided 95% prediction interval upper limit of 0.022 rad. The median (IQR) positioning error using the previous four-sphere registration fiducial assembly was 1.87 mm (1.77-2.14mm). This was found to be significantly different (p = 0.0012) from the median (IQR) positioning error of 0.28 mm (0.14-0.95 mm) using the new registration fiducial assembly on the same trajectories. No significant difference was detected between the medians of the angular errors (p = 0.26). This is the first study presenting an improved registration method and validation in tissue-mimicking phantoms of our remotely actuated MR-compatible mechatronic system for delivery of prostate FLA needles. Accounting for the effects of needle deflection, the system was demonstrated to be capable of needle delivery with an error of 4.13 mm or less in 95% of cases under ideal conditions, which is a statistically significant improvement over the previous method. The system will next be validated in a clinicalsetting.

The other sheep, resistant but forgotten: Archetypal characterization of Linca sheep farmers in Argentine Patagonia

Identifying and understanding how resilience is generated in a local socioecological system is essential for the design of future conservation strategies. The Linca sheep represents one of the most important – but least known – biocultural legacies of the Mapuche people in Patagonia. These sheep, which arrived in the region along with the conquistadors more than three centuries ago, have been almost completely displaced by the Merino sheep breed. In this work we analyse the principal components that characterise the relictual producers of Linca sheep. These components are the key for the conservation of this traditional practice and takes a substantial part of local zoological knowledge (LZK). Interviews with artisans (n = 51) who know, require or use Linca wool, either know the breeders, enabled us to contact and interview 13 families who preserve these sheep. Based on an interpretative study we identified 3 biocultural components and 11 analytical variables. Through this analysis of the archetypes, within this universe of producers, we recognized three profiles: Veteran, Tenacious, and Emotionally Attached. The families who have preserved this ancient practice were assigned to one of the three groups based on the shortest Euclidean distance and information obtained from the interviews. Among the main characteristics of these relictual producers the important role played by women stands out, as they preserve and transmit knowledge of the textile language and maintain the diversity of the Linca sheep, transforming their family units into guardians of the biocultural inheritance of this local breed.

Measurement of morphological features of Oncomelania hupensis shells in Yunnan Province

To investigate the morphological variation of Oncomelania hupensis shells in Yunnan Province, so as to provide insights into the understanding of O. hupensis genetic evolution and control. According to the O. hupensis density, geographical location, altitude, water system and environmental type, 12 administrative villages were sampled from 10 schistosomiasis-endemic counties (districts) in 3 prefectures (cities) of Yunnan Province as snail collection sites. From December 2021 to January 2022, about 200 snails were collected from each collection site, among which thirty adult snails (6 to 7 spirals) were randomly selected from each site, and the 11 morphological indexes of snail shells were measured and subjected to cluster analysis and principal component analysis. Of O. hupensis snails from 12 localities of Yunnan Province, the longest shell (7.33 mm) was detected in snails from Yongle Village, Eryuan County, with the shortest (4.68 mm) in Dongyuan Village, Gucheng District, and the largest angle of apex (59.47°) was measured in snails from Caizhuang Village, Midu County, with the smallest (41.40°) in Qiandian Village, Eryuan County. The mean coefficient of variation was 9.075% among O. hupensis snails from 12 localities of Yunnan Province, with the largest coefficient of variation seen in the thickness of the labra brim (29.809%). Among O. hupensis snails from 12 localities of Yunnan Province, the mean Euclidean distance was 2.26, with the shortest Euclidean distance seen between O. hupensis snails from Qiandian Village of Eryuan County and Wuxing Village of Dali City (0.26), and the largest found between O. hupensis snails from Caizhuang Village of Midu County and Cangling Village of Chuxiong County (8.17). Cluster analysis and principal component analysis classified O. hupensis snails from 12 localities of Yunnan Province into three categories, including the O. hupensis snail samples from Caizhuang Village of Midu County, O. hupensis snail samples from Cangling Village of Chuxiong County, and O. hupensis snail samples from Qiandian Village of Eryuan County, Wuxing Village of Dali City, Yangwu Village of Yongsheng County, Xiaoqiao Village of Xiangyun County, Yongle Village of Eryuan County, Xiaocen Village of Dali City, Anding Village of Nanjian County, Dongyuan Village of Gucheng District, Lianyi Village of Heqing County, and Dianzhong Village of Weishan County. The variations in these three categories of snail samples were mainly measured in the principal component 2 related to the angle of apex and the thickness of the labra brim. The variations in the Euclidean distance and morphological features of shells of O. hupensis from 12 localities of Yunnan Province gradually rise with the decrease in the latitude of the collection sites. The angle of apex is an indicator for the growth of O. hupensis whorl.

Geological boundary modeling with uncertainty using an indicator interpolated threshold approach

ABSTRACT Estimating the quality and quantity of minerals is an important step in evaluating the feasibility of a mining project. Before estimation of resources occurs, the domain extents must be defined. Uncertainty in the placement of boundaries is ubiquitous, and proper evaluation of uncertainty is integral to aiding subsequent engineering decisions. Implicit modeling of boundaries is a popular technique as it is data driven, fast, and automatic. Signed distance functions (SDF) are commonly used in implicit boundary modeling. The SDF in its basic form is the signed-dependent shortest Euclidean distance between data that are not of the same category. However, in the presence of spatial-data asymmetry, the SDF introduces a conservative bias leading to lower global tonnages for estimating resources. Moreover, uncertainty through an additive constant to the SDF results in homogenous and unreasonable uncertainty. A novel approach to implicit boundary modeling with uncertainty is to interpolate a field of probabilities from indicator data and threshold the estimate for boundary extraction. Uncertainty is captured by varying the indicator thresholds, which provides eroded and dilated boundaries. The result is a globally unbiased boundary model that closely follows the structure of the conditioning data and provides a realistic uncertainty bandwidth.

Abstract 1682: Imaging mass cytometry reveals key spatial features among immune cells in hepatocellular carcinomas treated with neoadjuvant cabozantinib and nivolumab

Abstract Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and carries a poor prognosis. Most HCC is unresectable at presentation, and until recently, the use of perioperative systemic therapy has been hampered by a lack of any effective therapies. We recently conducted a single-arm trial of neoadjuvant cabozantinib followed by nivolumab for borderline resectable or locally advanced HCC (NCT03299946), through which secondary resectability was obtained in a subset of patients. Of 15 patients enrolled, 12/15 patients achieved margin-negative resection, and 5/12 resected patients experienced a major or complete pathologic response. Here we characterize changes in the tumor microenvironment (TME) induced by neoadjuvant therapy. We constructed a tissue microarray containing 37 core liver biopsies (15 from responders, 22 from nonresponders), stained with a 27-marker panel, and used ilastik and CellProfiler to segment the resulting images, producing a single-cell dataset comprising 59,453 cells. We then used FlowSOM to perform unbiased clustering of cells, which we annotated into 17 cell types. Next, we performed spatial analysis using Voronoi diagrams and top neighbors mapping. We generated a minimum spanning tree using shortest Euclidean distances to model the simplest spatial relationships among all cell types and ranked their importance using random forest models. Grossly, responder cores were characterized by the presence of tertiary lymphoid aggregates, as well as a higher percent abundance of several immune cell types, including CD4 T (p < 0.05) and CD8 T cells (p < 0.005). In responders, Voronoi diagrams revealed denser packing of most immune cell types, particularly B cells (p < 0.005), and top neighbors analysis indicated higher numbers of lymphoid-lymphoid, myeloid-myeloid, and lymphoid-myeloid neighbors. This suggests that response is characterized by immune infiltration of the TME. Exploring this further, a minimum spanning tree showed that in nonresponders, CD8 T cells were flanked by CD163+ macrophages, whereas in responders, HCC cells were closely linked to lymphoid cells. Importance plots from random forest models for B, CD4 T, and CD8 T cells revealed that top predictors of responder status were higher minimum distance from CD163+ Arg1+ macrophages and lower minimum distance from CD163+ Ki67+ macrophages, which express higher levels of PD-L1. This suggests that proximity of B and T cells to macrophages that exert immunosuppression via Arg1 is a critical feature of resistance to cabozantinib plus nivolumab, whereas proximity to proliferative macrophages that express higher levels of PD-L1 is a key feature of response. In conclusion, cabozantinib and nivolumab can effectively promote antitumor immunity by altering both the abundance and spatial organization of macrophages, B cells, and T cells in the HCC TME. Citation Format: Shu Zhang, Qingfeng Zhu, Nicole Gross, Soren Charmsaz, Atul Deshpande, Stephanie Xavier, Aditya Mohan, James Leatherman, Guanglan Mo, Jennifer Durham, Aleksandra Popovic, Brad Wilt, Dongxia Lin, Derek Quong, Robert Anders, Elana Fertig, Elizabeth M. Jaffee, Mark Yarchoan, Won J. Ho. Imaging mass cytometry reveals key spatial features among immune cells in hepatocellular carcinomas treated with neoadjuvant cabozantinib and nivolumab [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1682.

Estimation and construction of confidence intervals for biomarker cutoff-points under the shortest Euclidean distance from the ROC surface to the perfection corner.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive type of cancer with a 5-year survival rate of less than 5%. As in many other diseases, its diagnosis might involve progressive stages. It is common that in biomarker studies referring to PDAC, recruitment involves three groups: healthy individuals, patients that suffer from chronic pancreatitis, and PDAC patients. Early detection and accurate classification of the state of the disease are crucial for patients' successful treatment. ROC analysis is the most popular way to evaluate the performance of a biomarker and the Youden index is commonly employed for cutoff derivation. The so-called generalized Youden index has a drawback in the three-class case of not accommodating the full data set when estimating the optimal cutoffs. In this article, we explore the use of the Euclidean distance of the ROC to the perfection corner for the derivation of cutoffs in trichotomous settings. We construct an inferential framework that involves both parametric and nonparametric techniques. Our methods can accommodate the full information of a given data set and thus provide more accurate estimates in terms of the decision-making cutoffs compared with a Youden-based strategy. We evaluate our approaches through extensive simulations and illustrate them on a PDAC biomarker study.

A forecasting of stock trading price using time series information based on big data

Big data is a large set of structured or unstructured data that can collect, store, manage, and analyze data with existing database management tools. And it means the technique of extracting value from these data and interpreting the results. Big data has three characteristics: The size of existing data and other data (volume), the speed of data generation (velocity), and the variety of information forms (variety). The time series data are obtained by collecting and recording the data generated in accordance with the flow of time. If the analysis of these time series data, found the characteristics of the data implies that feature helps to understand and analyze time series data. The concept of distance is the simplest and the most obvious in dealing with the similarities between objects. The commonly used and widely known method for measuring distance is the Euclidean distance. This study is the result of analyzing the similarity of stock price flow using 793,800 closing prices of 1,323 companies in Korea. Visual studio and Excel presented calculate the Euclidean distance using an analysis tool. We selected “000100” as a target domestic company and prepared for big data analysis. As a result of the analysis, the shortest Euclidean distance is the code “143860” company, and the calculated value is “11.147”. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

Yield stability analysis of maize hybrids using the self-organizing map of Kohonen

The purpose of this study is to classify 32 commercial maize hybrids with regard to grain yield stability by using an artificial neural network procedure. The hybrids were evaluated at five locations, in two late growing seasons. Each replication (R1 and R2) of the response variable was used as a network input signal to trigger the network learning process. The underlying network model has a topology consisting of two neurons in the input layer and ten neurons arranged in a two-dimensional grid. The competitive process was induced by the random presentation of an input vector $$x = [x_{1} ,x_{2} , \ldots ,x_{n} ]^{{\text{T}}}$$ from the network training set, without specifying a desired output. A grid neuron y responded best to this stimulus. Thus, the neuron with the shortest Euclidean distance between the input vector and the respective weight vector $$w_{i} = [w_{i1} ,w_{i2} , \ldots ,w_{in} ]^{{\text{T}}}$$ , at moment t, was selected as the winner. The winning neuron indicates the center of a topological neighborhood of cooperative neurons. The adaptive process occurred via applying an adjustment Δwij to the synaptic weights wij during learning, until convergence of the network. The results showed that the classes of hybrids with the same performance pattern across environments were not altered by the network, confirming the high yield stability and satisfactory overall performance associated with higher grain yield means (above 6 t ha−1). The single-cross hybrid 10 (CD-387) stood out at all locations in both years, with unaltered data classification by the network. Therefore, it was considered to be stable in all environments, without performance variation over the years, as well as adaptable.

OPTIMUM PATH DETERMINATION TO FACILITATE FIRE STATION RESCUE MISSIONS USING ANT COLONY OPTIMIZATION ALGORITHMS (CASE STUDY: CITY OF KARAJ)

Abstract. The successful conduct of a rescue mission in urban areas is directly related to the timely deployment of equipment and personnel to the incident location which justifies the quest for optimum path selection for emergency purposes. In this study, it is attempted to use Ant Colony Optimization (ACO) to find the optimum paths between fire stations and incident locations. It is also attempted to build up an evaluation tool using ACO to detect critical road segments that the overall accessibility to fire station services throughout the urban area is constituted upon their excellent functionality. Therefore, an ACO solution is designed to find optimum paths between the fire station and some randomly distributed incident locations. Regarding different variants of ACO, the algorithm enjoys the Simple Ant Colony Optimization deployment strategy combined with Ant Algorithm Transition rules. Iteration best pheromone updating is also used as the pheromone reinforcement strategy. The cost function used to optimize the path considers the shortest Euclidean distance on the network. The results explicitly state that the proposed method is successful to create the optimum path in 95.45 percent of all times, compared to Dijkstra deterministic approaches. Moreover, the pheromone map as an indicator of the criticality of road elements is generated and discussed. Visual inspection shows that the pheromone map is verified as the road criticality map concerning fire station access to the region and therefore pre-emptive measures can be defined by analyzing the generated pheromone map.

Machine learning based null allocation design for adaptive beamforming in unmanned aerial vehicle communications

In order to communicate with target unmanned aerial vehicles (UAVs), ground control stations (GCSs) typically adopt adaptive beamforming with high antenna gain and co-channel interference rejection. Multiple interfering signals arriving from different directions arise from other UAVs and other GCSs, and the beamformer installed in the home GCS will usually attempt to null unwanted signals from all these directions of arrival (DoAs) without analyzing the distribution of the angles of arrival. Consequently, the beamformer will fail to allocate nulls in some directions, and the signal-to-interference-plus-noise (SINR) performance of the home GCS is impaired. In this paper, a new approach to null allocation is proposed, based on machine learning using k-means clustering. The design first involves the collection of information about the DoAs and the corresponding received signal strengths of all the interfering signals into a two-dimensional dataset. Secondly, this dataset is broken down into clusters by using k-means clustering, and the cluster centroids are calculated. In each cluster, the interfering signal that has the shortest Euclidean distance to the centroid is identified as the approximated centroid. Only the approximated centroids are selected as input to the beamformer, with the aim that each complete cluster of interference sources can be nulled by allocating one null per cluster. To optimize the number of clusters k used in the null allocation process, the design adopts the particle swarm optimization technique to adaptively update the value of k to maximize the SINR at the home GCS. Simulation results show that our design yields a maximum SINR improvement of about 12 dB when compared to cases where no null allocation is considered. Moreover, our design also outperforms null steering in the UAV scenarios. Advantageously, this enhanced performance is obtained without the need for additional power amplification or hardware modification to the beamformer.

Linear- and Linear-Matrix-Inequality-Constrained State Estimation for Nonlinear Systems

This paper considers nonlinear state estimation subject to inequality constraints in the form of linear and linear-matrix inequalities. Rewriting the standard maximum likelihood objective function used to derive the Kalman filter allows the Kalman gain to be found by solving a constrained optimization problem with a linear objective function subject to a linear-matrix-inequality constraint. Additional constraints, such as weighted-norm- or linear-inequality constraints, that the state estimate must satisfy are easily augmented to the constrained optimization problem. The proposed constrained estimation methodology is applied in the extended Kalman filter (EKF) and sigma point Kalman filter (SPKF) frameworks. Motivated by estimation problems involving a vehicle that can rotate and translate in space, multiplicative versions of the constrained EKF and SPKF formulations are discussed. Simulation results for a ground-based mobile robot operating in a constrained three-dimensional terrain are presented and are compared to results that use the traditional multiplicative EKF and SPKF, as well as filters that enforce inequality constraints by simply projecting the state estimate into the constrained domain along the shortest Euclidean distance.

Application of the TOPSİS method to improve software efficiency and to optimize its management

Technique for Order of Prevention by Similarity to Ideal Solution (TOPSIS) method is used for decision-making to improve software efficiency and to optimize its management by using methodological approaches. TOPSIS method is a multi-criteria decision-making analysis method. TOPSIS identifies the best alternative variant based on compromise solution. The basic concept of the TOPSIS method is that the chosen alternate variant has to be at the shortest Euclidean distance from the positive ideal solution and at the farthest Euclidean distance from the negative ideal solution. Criteria and alternatives for software are identified. Two or three of software features from other studies are used. Based on international experience, practically, only few characteristics of software efficiency have been used in the articles so far, but not all eight characteristics. Eight attributes of software efficiency are used, which distinguish this study from others. The values of the worst and best alternatives are found in multi-criterion decision-making by using the estimations of four expert programmers. The software currently run in three systems was used in experiments. The skills of the experts are also taken into account for finding the values. The results of the experiments are estimated to be good.

Tree Composition and Ecological Structure of Akak Forest Area

Tree composition and ecological structure were assessed in Akak forest area with the objective of assessing the floristic composition and the regeneration potentials. The study was carried out between April 2018 to February 2019. A total of 49 logged stumps were selected within the Akak forest spanning a period of 5 years and 20m x 20m transects were demarcated. All plants species <1cm and above were identified and recorded. Results revealed that a total of 5239 individuals from 71 families, 216 genera and 384species were identified in the study area. The maximum plants species was recorded in the year 2015 (376 species). The maximum number of species and regeneration potentials was found in the family Fabaceae, (99 species) and (31) respectively. Baphia nitida, Musanga cecropioides and Angylocalyx pynaertii were the most dominant plants specie in the years 2013, 2015 and 2017 respectively. The year 2017 depicts the highest Simpson diversity with value of (0.989) while the year 2015 show the highest Simpson dominance with value of (0.013). The year 2013 show a highest Shannon evenness with value of (0.4879). Logged compartment 2015 has a highest fisher alpha with value of 137.7 depicting highest specie richness The Shortest Euclidean distance of 123.44 between year 2013 and 2017 show that they both have many plants species that are similar. Evidently the forest area is very rich in trees in the lower diameter classes, 
 
 and the structure of the Akak forest area is J reverse indicating that the forest is growing to climax.

Facial image retrieval on semantic features using adaptive mean genetic algorithm

The emergence of larger databases has made image retrieval techniques an essential component and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying the Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.

Facial Image Retrieval on Semantic Features Using Adaptive Genetic Algorithm

The emergence of larger databases has made image retrieval techniques an essential component, and has led to the development of more efficient image retrieval systems. Retrieval can either be content or text-based. In this paper, the focus is on the content-based image retrieval from the FGNET database. Input query images are subjected to several processing techniques in the database before computing the squared Euclidean distance (SED) between them. The images with the shortest Euclidean distance are considered as a match and are retrieved. The processing techniques involve the application of the median modified Weiner filter (MMWF), extraction of the low-level features using histogram-oriented gradients (HOG), discrete wavelet transform (DWT), GIST, and Local tetra pattern (LTrP). Finally, the features are selected using Adaptive Mean Genetic Algorithm (AMGA). In this study, the average PSNR value obtained after applying Wiener filter was 45.29. The performance of the AMGA was evaluated based on its precision, F-measure, and recall, and the obtained average values were respectively 0.75, 0.692, and 0.66. The performance matrix of the AMGA was compared to those of particle swarm optimization algorithm (PSO) and genetic algorithm (GA) and found to perform better; thus, proving its efficiency.

Image processing-based contour parallel tool path optimization for arbitrary pocket shape

Contour-parallel tool path has been a popular means in milling 2D pocket regions. Traditional contour-parallel path suffers from unsmooth transition and uncut problem around corners, which requires dedicated local geometric treatment. In addition, these two issues contradict with each other; solving one may jeopardize the other. Motivated by these problems, we present a digital image approach to deal with the contour parallel tool path optimization. By initially converting the pocket boundary into a binary image, a greyscale image is constructed via an inward-marching algorithm, such that the greyscale value of every pixel inside the pocket indicates the shortest Euclidean distance (SED) towards the boundary. The contour-parallel tool path can be easily derived from this SED image as the iso-contours. Distinguished from the geometry-based treatments towards unsmooth tool path and uncut regions, the proposed framework takes advantage of various image processing techniques (such as Gaussian blur and unsharp masking) to the SED image. By adaptively optimizing the parameters for image blurring and sharpness, the corresponding iso-SED tool path eliminates the uncut residues while still satisfies a specified level of smoothness. A quantitative analysis together with an image optimization scheme is also presented in this work.

Sonographic prediction of macrosomia in pregnancies complicated by maternal diabetes: finding the best formula.

To evaluate the best performing formula for macrosomia prediction in pregnancies complicated by diabetes. A retrospective analysis was performed of 1060 sonographic fetal biometrical measurements performed within 7days of delivery in term pregnancies (37-42 gestational weeks) complicated by diabetes. Sonographic prediction of macrosomia (≥ 4000, ≥ 4250, and ≥ 4500 g) was evaluated utilizing ten previously published formulas by: (1) calculating for each macrosomia threshold the sensitivity, specificity, positive and negative predictive value, and ± likelihood ratio for macrosomia prediction; (2) comparing the systematic and random error and the proportion of estimates < 10% of birth weights between macrosomic and non-macrosomic neonates. Best performing formula was determined based on Euclidean distance. 97 (9.2%) macrosomic neonates (> 4000 g) were included. Median birth weight was 3380 (1866-3998)g for non-macrosomic and 4198 (4000-5180)g for macrosomic neonates. Higher macrosomia cutoff was associated with higher specificity and lower sensitivity. We found a considerable variation between formulas in different accuracy parameters. Hadlock's formula (1985), based on abdominal circumference, femur length, head circumference and biparietal diameter, had the shortest Euclidean distance, reflecting the highest accuracy. Prediction of macrosomia among women with diabetes differs significantly between formulas. In our cohort, the best performing formula for macrosomia prediction was Hadlock's formula (1985).

Coded Eight-Dimensional QAM Technique Using Iterative Soft-Output Decoding and Its Demonstration in High Baud-Rate Transmission

We proposed coded eight-dimensional (8-D) quadrature amplitude modulation (QAM) and experimentally demonstrated in high baud-rate transmission. Both high baud rate and multilevel modulation techniques are necessary for large capacity long haul transmission without increasing the number of transponders. However, optical signal-to-noise ratio (OSNR) tolerance of higher order modulation is low due to the short minimum Euclidean distance. Therefore, to relax OSNR tolerance by enlarging minimum Euclidean distance, we proposed 8-D modulation formats. The spectral efficiency of the proposed coded 8-D-16QAM, 8-D-32QAM, and 8-D-64QAM is equivalent to polarization division multiplexed (PDM)-8QAM, PDM-16QAM, and PDM-32QAM, respectively. To confirm the applicability of the proposed 8-D modulation to high baud rate transmission, we experimentally demonstrated 96-GBaud 8-D-16QAM wavelength division multiplexed transmission over 5,-252 km using iterative soft output decoding. The line data rate is 576-Gbps and a net rate 478-Gbps. At the same spectral efficiency, 8-D-16QAM using iterative soft output decoding can expand the transmission reach by 1212 km compared to conventional PDM-8QAM.

Selection of substrate material for hybrid microwave integrated circuits (HMICs)

This paper presents systematic approach of material selection for substrate material in hybrid microwave integrated circuits (HMICs). The performance of HMICs starts deteriorating at microwave frequencies because the circuit dimensions become comparable to the wavelength. So, it is important to design HMICs in such a way that its performance remains unaffected even at high frequencies. One way to fulfil this requirement is to fabricate a proper device. For this, substrate material in HMICs plays an important role. But there are different types of materials available which can be used as substrate material of HMICs. The commonly reported materials are: Silicon (Si), Gallium Arsenide (GaAs), Indium Phosphide (InP), Alumina (Al2O3), Beryllia, Fused Silica, Sapphire, Woven PTFF/ Glass, PTFE/Glass, Polyolefin, and Ferrite/Granite. As each material is having its own advantages and limitations; therefore, it is important to choose the best possible material out of all possible materials. For this purpose two decision making techniques are used. Firstly, Ashby’s approach as the Multi-Objective Decision Making (MODM) technique is used, and then the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as the Multiple Attribute Decision Making (MADM) technique is used. Ashby’s approach provides effective material selection technique to select the best material based on their material and performance indices for better device performance. While TOPSIS approach is a decision making approach which is basically used to determine the best alternative which should have the shortest Euclidean distance (S*) from the ideal solution (A*) and the largest Euclidean distance (S-) from the negative ideal solution (A-). For MICs, the material indices considered and evaluated are surface roughness, relative dielectric constant, loss tangent, thermal conductivity, and dielectric strength. It is observed that Sapphire followed by high-purity Alumina (99.5 percent pure or better) are good candidates for substrate material in hybrid MICs. The surface roughness of Sapphire is better than that of Alumina, though the dielectric strength of both these materials is the same. A comparison between two different methods for material selection shows consistency between the two methods of material selection. Both methodologies show that Sapphire is the most suitable substrate material for hybrid MICs.