How to predict social relationships — Physics-inspired approach to link prediction

© 2019 Elsevier B.V. Link prediction in social networks has a long history in complex network research area. The formation of links in networks has been approached by scientists from different backgrounds, ranging from physics to computer science. To predict the formation of new links, we consider measures which originate from network science and use them in the place of mass and distance within the formalism of Newton's Gravitational Law. The attraction force calculated in this way is treated as a proxy for the likelihood of link formation. In particular, we use three different measures of vertex centrality as mass, and 13 dissimilarity measures including shortest path and inverse Katz score in place of distance, leading to over 50 combinations that we evaluate empirically. Combining these through gravitational law allows us to couple popularity with similarity, two important characteristics for link prediction in social networks. Performance of our predictors is evaluated using Area Under the Precision–Recall Curve (AUC)for seven different real-world network datasets. The experiments demonstrate that this approach tends to outperform the setting in which vertex similarity measures like Katz are used on their own. Our approach also gives us the opportunity to combine network's global and local properties for predicting future or missing links. Our study shows that the use of the physical law which combines node importance with measures quantifying how distant the nodes are, is a promising research direction in social link prediction

Optimizing Signal Detection in MIMO Systems:AI vs Approximate and Linear Detectors

Artificial intelligence has transformed multiple input multiple output (MIMO) technology into a promising candidate for six-generation networks. However, several interference signals impact the data transmission between various antennas; therefore, sophisticated signal detection techniques are required at the MIMO receiver to estimate the transmitted data. This paper presents an optimized AI-based signal detection technique called AIDETECT for MIMO systems. The proposed AIDETECT network model is developed based on an optimized deep neural network (DNN) architecture, whose efficiency lies in its lightweight network architecture. To train and test the AIDETECT network model, we generate and process the data in a suitable form based on the transmitted signal, channel information, and noise. Based on this data, we calculate the received signal at the receiver end, where the received signal and channel information were integrated into the AIDETECT network model to perform reliable signal detection. Simulation results show that at a 20-dB signal-to-noise ratio (SNR), the proposed AIDETECT technique achieves between 97.33% to 99.99% better performance compared to conventional MIMO detectors and is also able to accomplish between 25.34% to 99.98% better performance than other AI-based MIMO detectors for the considered performance metrics. In addition, due to lightweight network architecture, the proposed AIDETECT technique has also achieved much lower computational complexity than conventional and AI-based MIMO detectors.</p

Simulation: Early Detection of Brain Vessels Stroke by Applying Electromagnetic Waves Non-Invasively

Introduction: Early recognition of stroke with its two types Ischemic and Hemorrhagic, is one of the most crucial research points, commonly used methods are CT- (computerized tomography), and MRI- (Magnetic resonance imaging). These techniques cause a delay in the detection of the condition, which causes permanent disability. The main reason behind the fatal consequences of stroke is the delay of detection. Therefore, this research paper aims to early detection of the type of stroke without delay until the appropriate diagnosis of each type is made, and then the appropriate treatment without delay. Method: Using a non-invasive and fast technique to determine the stroke type by wave, we simulate and design a vessel containing a liquid as a laminar flow with the same density and velocity of blood, and it was surrounded by a Homogenized multi-turn coil consisting of (n) turns to represent the magnetic field, using specific frequency (HZ) with Electrical field in coil current (A) to see the changing in magnetic flux density (MFD), Depending on the changes in MFD, the flow of blood in laminar flow can be affected by clotting (Ischemic) or Hemorrhagic (cutting) in our vessel designed. We have built three different scenarios to apply the technique which are: First: Normal Scenario (where the blood in vessel has no problem), second: clotting (ischemic, where the vessel blocked in specific three position) and Third: Cutting (Hemorrhagic, where the vessel cut in certain nine positions). Results: This paper presents-through our own design-the studying of applying the electromagnetic waves on blood inside the vessel to detect the stroke type in our three scenarios (normal, ischemic three positions or hemorrhagic nine positions), Studying the magnetic field and laminar flow. This study covered in three areas. First: coil geometry analysis, Second: stationary, and Third: frequency domain. through the changes in Magnetic Flux Density -MFD- waves. The results were promising and distinct for distinguishing between the three scenarios which are normal, ischemic (3 positions) and hemorrhagic (9 positions) the results of MFD are: 0.09 to 3.3*10^-3, 0.08 to 3.15*10^-4, 0.15 to 6.2*10^-3 respectively

Simulation and Augmentation of Social Networks for Building Deep Learning Models

A limitation of the Graph Convolutional Networks (GCNs) is that it assumes at a particular $l^{th}$ layer of the neural network model only the $l^{th}$ order neighbourhood nodes of a social network are influential. Furthermore, the GCN has been evaluated on citation and knowledge graphs, but not extensively on friendship-based social graphs. The drawback associated with the dependencies between layers and the order of node neighbourhood for the GCN can be more prevalent for friendship-based graphs. The evaluation of the full potential of the GCN on friendship-based social network requires openly available datasets in larger quantities. However, most available social network datasets are not complete. Also, the majority of the available social network datasets do not contain both the features and ground truth labels. In this work, firstly, we provide a guideline on simulating dynamic social networks, with ground truth labels and features, both coupled with the topology. Secondly, we introduce an open-source Python-based simulation library. We argue that the topology of the network is driven by a set of latent variables, termed as the social DNA (sDNA). We consider the sDNA as labels for the nodes. Finally, by evaluating on our simulated datasets, we propose four new variants of the GCN, mainly to overcome the limitation of dependency between the order of node-neighbourhood and a particular layer of the model. We then evaluate the performance of all the models and our results show that on 27 out of the 30 simulated datasets our proposed GCN variants outperform the original model

Seatbelts and road traffic collision injuries

Modification of seatbelts and their legislation played an important role in reducing morbidity and mortality of occupants in road traffic collisions. We aimed to review seatbelt development, its mechanism of action and its effects. Seatbelts reduce injury by preventing the occupant from hitting the interior parts of the vehicle or being ejected from the car. We have made a linear regression correlation between the overall seatbelt compliance and road traffic death rates in 46 high income countries to study the relationship between seatbelt use and mortality. There was a very highly significant negative correlation between the seatbelt compliance and road traffic death rates (R = - 0.77, F = 65.5, p < 0.00001). Seatbelt-related injuries include spinal, abdominal or pelvic injuries. The presence of a seatbelt sign must raise the suspicion of an intra-abdominal injury. These injuries can be reduced if seatbelts were applied correctly. Although seatbelts were recognized as an important safety measure, it still remains underused in many countries. Enforcement of seatbelt usage by law is mandatory so as to reduce the toll of death of road traffic collisions

Creation and Spatial Analysis of 3D City Modeling based on GIS Data

The 3D city model is one of the crucial topics that are still under analysis by many engineers and programmers because of the great advancements in data acquisition technologies and 3D computer graphics programming. It is one of the best visualization methods for representing reality. This paper presents different techniques for the creation and spatial analysis of 3D city modeling based on Geographical Information System (GIS) technology using free data sources. To achieve that goal, the Mansoura University campus, located in Mansoura city, Egypt, was chosen as a case study. The minimum data requirements to generate a 3D city model are the terrain, 2D spatial features such as buildings, landscape area and street networks. Moreover, building height is an important attribute in the 3D extrusion process. The main challenge during the creation process is the dearth of accurate free datasets, and the time-consuming editing. Therefore, different data sources are used in this study to evaluate their accuracy and find suitable applications which can use the generated 3D model. Meanwhile, an accurate data source obtained using the traditional survey methods is used for the validation purpose. First, the terrain was obtained from a digital elevation model (DEM) and compared with grid leveling measurements. Second, 2D data were obtained from: the manual digitization from (30 cm) high-resolution imagery, and deep learning structure algorithms to detect the 2D features automatically using an object instance segmentation model and compared the results with the total station survey observations. Different techniques are used to investigate and evaluate the accuracy of these data sources. The procedural modeling technique is applied to generate the 3D city model. TensorFlow & Keras frameworks (Python APIs) were used in this paper; moreover, global mapper, ArcGIS Pro, QGIS and CityEngine software were used. The precision metrics from the trained deep learning model were 0.78 for buildings, 0.62 for streets and 0.89 for landscape areas. Despite, the manual digitizing results are better than the results from deep learning, but the extracted features accuracy is accepted and can be used in the creation process in the cases not require a highly accurate 3D model. The flood impact scenario is simulated as an application of spatial analysis on the generated 3D city model. Doi: 10.28991/CEJ-2022-08-01-08 Full Text: PD

Folate Conjugated Nanomedicines for Selective Inhibition of mTOR Signaling in Polycystic Kidneys at Clinically Relevant Doses

Although rapamycin is a very effective drug for rodents with polycystic kidney disease (PKD), it is not encouraging in the clinical trials due to the suboptimal dosages compelled by the off-target side effects. We here report the generation, characterization, specificity, functionality, pharmacokinetic, pharmacodynamic and toxicology profiles of novel polycystic kidney-specific-targeting nanoparticles (NPs). We formulated folate-conjugated PLGA-PEG NPs, which can be loaded with multiple drugs, including rapamycin (an mTOR inhibitor) and antioxidant 4-hydroxy-TEMPO (a nephroprotective agent). The NPs increased the efficacy, potency and tolerability of rapamycin resulting in an increased survival rate and improved kidney function by decreasing side effects and reducing biodistribution to other organs in PKD mice. The daily administration of rapamycin-alone (1 mg/kg/day) could now be achieved with a weekly injection of NPs containing rapamycin (379 μg/kg/week). This polycystic kidney-targeting nanotechnology, for the first time, integrated advances in the use of 1) nanoparticles as a delivery cargo, 2) folate for targeting, 3) near-infrared Cy5-fluorophore for in vitro and in vivo live imaging, 4) rapamycin as a pharmacological therapy, and 5) TEMPO as a combinational therapy. The slow sustained-release of rapamycin by polycystic kidney-targeting NPs demonstrates a new era of nanomedicine in treatment for chronic kidney diseases at clinically relevant doses

Sheared bioconvection in a horizontal tube

The recent interest in using microorganisms for biofuels is motivation enough to study bioconvection and cell dispersion in tubes subject to imposed flow. To optimize light and nutrient uptake, many microorganisms swim in directions biased by environmental cues (e.g. phototaxis in algae and chemotaxis in bacteria). Such taxes inevitably lead to accumulations of cells, which, as many microorganisms have a density different to the fluid, can induce hydrodynamic instabilites. The large-scale fluid flow and spectacular patterns that arise are termed bioconvection. However, the extent to which bioconvection is affected or suppressed by an imposed fluid flow, and how bioconvection influences the mean flow profile and cell transport are open questions. This experimental study is the first to address these issues by quantifying the patterns due to suspensions of the gravitactic and gyrotactic green biflagellate alga Chlamydomonas in horizontal tubes subject to an imposed flow. With no flow, the dependence of the dominant pattern wavelength at pattern onset on cell concentration is established for three different tube diameters. For small imposed flows, the vertical plumes of cells are observed merely to bow in the direction of flow. For sufficiently high flow rates, the plumes progressively fragment into piecewise linear diagonal plumes, unexpectedly inclined at constant angles and translating at fixed speeds. The pattern wavelength generally grows with flow rate, with transitions at critical rates that depend on concentration. Even at high imposed flow rates, bioconvection is not wholly suppressed and perturbs the flow field.Comment: 19 pages, 9 figures, published version available at http://iopscience.iop.org/1478-3975/7/4/04600