Journal articles on the topic 'Selection network (SN)'
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Höfinger, Gerhard, and Stefan Brunner. "Network-based Simulation in Water Construction – a Flexible Tool for Equipment Selection." SNE Simulation Notes Europe 26, no. 1 (March 2016): 55–58. http://dx.doi.org/10.11128/sne.26.sn.10329.
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He, Yixin, Daosen Zhai, Dawei Wang, Xiao Tang, and Ruonan Zhang. "A Relay Selection Protocol for UAV-Assisted VANETs." Applied Sciences 10, no. 23 (December 7, 2020): 8762. http://dx.doi.org/10.3390/app10238762.
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In this paper, we investigate the relay selection problem for the unmanned aerial vehicle (UAV)-assisted vehicular ad-hoc networks (VANETs). For the considered network, we first model and analyze the link quality of service (LQoS) from the source node (SN) to the neighbor node and the node forward capacity (NFC) from the neighbor node to the destination node (DN). Then, the relay selection problem is formulated as a multi-objective optimization problem by jointly considering the LQoS and the NFC. Afterward, we decompose the problem into two subproblems and propose a relay selection protocol with the storage-carry-forward (SCF) method. Moreover, we define a utility function with the node encounter frequency (NEF) and the message time-to-live (TTL) taken into account, based on which a redundant copy-deleting approach is devised. Furthermore, we analyze the security of the designed protocol. Finally, the simulation results demonstrate that the proposed relay selection protocol can improve the message delivery ratio, reduce the average end-to-end delay, and limit the overhead.
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Deepa, C., B. Latha, and I. Jenish. "Development and Performance Evaluation of a New Energy-Efficient Double Cluster-Head Routing (EEDCR) Protocol for Wireless Sensor Networks." Wireless Communications and Mobile Computing 2022 (June 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/5041153.
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Utilization of energy and the lifetime increment are the big issues in designing of routing algorithms for wireless sensor networks (WSNs). Many routing algorithms have been developed by various researchers to achieve energy efficiency and to improve the lifetime of the network. But, the way to route the information from the sensor node (SN) to the base station (BS) and vice versa is an important issue, because of resource constraints. In this paper, we have proposed a low energy consumed, cluster-based routing protocol named an energy-efficient and double cluster-head routing (EEDCR) protocol, to increase the network lifetime and minimize the end-to-end delay. Selection of the cluster head (CH) is depending on random selection method, energy method, the total number of nodes, and its energy level. The performances of developed protocol were assessed using simulations and found that it provides successful outcomes in terms of energy-efficient and lifetime increment in routing for WSNs.
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Nasimifar, Mahdi, Senthilmurugan Thyagarajan, Sarah Chaudhari, and Nadarajah Sivaneswaran. "Pavement Structural Capacity from Traffic Speed Deflectometer for Network Level Pavement Management System Application." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 2 (January 19, 2019): 456–65. http://dx.doi.org/10.1177/0361198118825122.
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Structural number (SN) represents the structural capacity of a flexible pavement system to sustain anticipated traffic and is among the structural indices most commonly used by pavement design engineers in the U.S. Effective structural number (SNeff) is an indicator of structural capacity of in-service pavement sections and is conventionally estimated from nondestructive testing (NDT) device data such as falling weight deflectometers (FWDs) using methods such as suggested by AASHTO. In addition to pavement design, structural condition is a critical input for the selection of maintenance and rehabilitation strategies in pavement management system (PMS) application. However, use of SN in network level application has not been practical because of limitations of FWD such as stop-and-go operation, lane closures, and low testing frequency. The traffic speed deflectometer (TSD), a continuous deflection device, has recently been gaining worldwide application as a reliable NDT device for network level PMS applications. The objective of this study is to develop a practical approach to compute and utilize SN of in-service flexible pavements from TSD data for network level PMS applications. The study is based on the fundamental that, for the same pavement, SNeff from the TSD using the proposed method should be in good agreement with SNeff from the FWD using AASHTO method. The developed method was field validated with TSD and FWD data collected at in-service pavement sections. In addition, the use of structural number ratio, defined as a ratio of SNeff to required SN, in network level prioritization of structural capacity improvements was illustrated.
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Xiao, Xingxing, and Haining Huang. "A Clustering Routing Algorithm Based on Improved Ant Colony Optimization Algorithms for Underwater Wireless Sensor Networks." Algorithms 13, no. 10 (October 1, 2020): 250. http://dx.doi.org/10.3390/a13100250.
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Because of the complicated underwater environment, the efficiency of data transmission from underwater sensor nodes to a sink node (SN) is faced with great challenges. Aiming at the problem of energy consumption in underwater wireless sensor networks (UWSNs), this paper proposes an energy-efficient clustering routing algorithm based on an improved ant colony optimization (ACO) algorithm. In clustering routing algorithms, the network is divided into many clusters, and each cluster consists of one cluster head node (CHN) and several cluster member nodes (CMNs). This paper optimizes the CHN selection based on the residual energy of nodes and the distance factor. The selected CHN gathers data sent by the CMNs and transmits them to the sink node by multiple hops. Optimal multi-hop paths from the CHNs to the SN are found by an improved ACO algorithm. This paper presents the ACO algorithm through the improvement of the heuristic information, the evaporation parameter for the pheromone update mechanism, and the ant searching scope. Simulation results indicate the high effectiveness and efficiency of the proposed algorithm in reducing the energy consumption, prolonging the network lifetime, and decreasing the packet loss ratio.
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Orlandi, Andrea, and Alice Mado Proverbio. "Left-Hemispheric Asymmetry for Object-Based Attention: an ERP Study." Brain Sciences 9, no. 11 (November 8, 2019): 315. http://dx.doi.org/10.3390/brainsci9110315.
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It has been shown that selective attention enhances the activity in visual regions associated with stimulus processing. The left hemisphere seems to have a prominent role when non-spatial attention is directed towards specific stimulus features (e.g., color, spatial frequency). The present electrophysiological study investigated the time course and neural correlates of object-based attention, under the assumption of left-hemispheric asymmetry. Twenty-nine right-handed participants were presented with 3D graphic images representing the shapes of different object categories (wooden dummies, chairs, structures of cubes) which lacked detail. They were instructed to press a button in response to a target stimulus indicated at the beginning of each run. The perception of non-target stimuli elicited a larger anterior N2 component, which was likely associated with motor inhibition. Conversely, target selection resulted in an enhanced selection negativity (SN) response lateralized over the left occipito-temporal regions, followed by a larger centro-parietal P300 response. These potentials were interpreted as indexing attentional selection and categorization processes, respectively. The standardized weighted low-resolution electromagnetic tomography (swLORETA) source reconstruction showed the engagement of a fronto-temporo-limbic network underlying object-based visual attention. Overall, the SN scalp distribution and relative neural generators hinted at a left-hemispheric advantage for non-spatial object-based visual attention.
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Tsvetanov, Filip Atanasov, and Martin Pandurski. "Selection of Protocols for Integration of Sensory Data Networks in Cloud Structures." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 09 (July 11, 2022): 29–40. http://dx.doi.org/10.3991/ijoe.v18i09.31321.
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The striving to obtain more detailed information about the environment and control various processes leads to an increase in the number of connected sensor devices in various industrial areas. The collected large amount of data can be analysed in real-time. The sensors that build up the WSN have limited hardware resources and cannot process large amounts of data. The integration between WSN and cloud structures is an excellent method for storing, processing, accessing data via the Internet and solves the issue of the limited capacity of WSN. The big challenge to designing the WSN - cloud systems is establishing a communication channel (through different protocols) between devices in the network and cloud platforms. This project executes/perform a real experiment on the XBee sensor network and the ThingSpeak cloud, and the data transmission between them is forwarded using different protocols (HTTP, HTTPS, MQTT and MQTT-SN). The influence of the parameters of the transmitted packet on the delay, the CPU, RAM load has been studied. The results give some advantages of MQTT over other protocols in terms of data rate, CPU and RAM load when working with XBee sensor modules and integration between WSN and cloud structures.
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Narayan, Vipul, and A. K. Daniel. "CHOP: Maximum Coverage Optimization and Resolve Hole Healing Problem using Sleep and Wake-up Technique for WSN." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 11, no. 2 (October 21, 2022): 159–78. http://dx.doi.org/10.14201/adcaij.27271.
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The Sensor Nodes (SN) play an important role in various hazardous applications environments such as military surveillance, forests, battlefield, etc. The Wireless Sensor Network (WSN) comprised multiple numbers of sensor nodes which are used to perform sensing the physical conditions and subsequently transmitting data to the Base Station (BS). The nodes have limited batteries. The random distribution of nodes in the hazardous areas causes overlapping of nodes and coverage hole issues in the network. The Coverage Optimization and Resolve Hole Healing (CHOP) Protocol is proposed to optimize the network's overlapping and resolve the coverage hole problem. The working phases of the proposed protocol are network initialization, formation of the cluster, Selection of Cluster Head, and sleep and wake-up phase. The issues are optimized, and maximum coverage is achieved for a specific sensing range. Using statistics and probability theory, a link is established between the radius of the node and the coverage area. The protocol used the sleep and wake phase to select optimal nodes active to achieve maximum coverage. The proposed protocol outperformed and showed improvements in the network's performance and lifetime compared to LEACH, TEEN, SEP, and DEEC protocols.
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Lo, Serigne N., Jiawen Ma, Richard A. Scolyer, Lauren E. Haydu, Jonathan R. Stretch, Robyn P. M. Saw, Omgo E. Nieweg, et al. "Improved Risk Prediction Calculator for Sentinel Node Positivity in Patients With Melanoma: The Melanoma Institute Australia Nomogram." Journal of Clinical Oncology 38, no. 24 (August 20, 2020): 2719–27. http://dx.doi.org/10.1200/jco.19.02362.
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PURPOSE For patients with primary cutaneous melanoma, the risk of sentinel node (SN) metastasis varies according to several clinicopathologic parameters. Patient selection for SN biopsy can be assisted by National Comprehensive Cancer Network (NCCN) and ASCO/Society of Surgical Oncology (SSO) guidelines and the Memorial Sloan Kettering Cancer Center (MSKCC) online nomogram. We sought to develop an improved online risk calculator using alternative clinicopathologic parameters to more accurately predict SN positivity. PATIENTS AND METHODS Data from 3,477 patients with melanoma who underwent SN biopsy at Melanoma Institute Australia (MIA) were analyzed. A new nomogram was developed by replacing body site and Clark level from the MSKCC model with mitotic rate, melanoma subtype, and lymphovascular invasion. The predictive performance of the new nomogram was externally validated using data from The University of Texas MD Anderson Cancer Center (n = 3,496). RESULTS The MSKCC model receiver operating characteristic curve had a predictive accuracy of 67.7% (95% CI, 65.3% to 70.0%). The MIA model had a predictive accuracy of 73.9% (95% CI, 71.9% to 75.9%), a 9.2% increase in accuracy over the MSKCC model ( P < .001). Among the 2,748 SN-negative patients, SN biopsy would not have been offered to 22.1%, 13.4%, and 12.4% based on the MIA model, the MSKCC model, and NCCN or ASCO/SSO criteria, respectively. External validation generated a C-statistic of 75.0% (95% CI, 73.2% to 76.7%). CONCLUSION A robust nomogram was developed that more accurately estimates the risk of SN positivity in patients with melanoma than currently available methods. The model only requires the input of 6 widely available clinicopathologic parameters. Importantly, the number of patients undergoing unnecessary SN biopsy would be significantly reduced compared with use of the MSKCC nomogram or the NCCN or ASCO/SSO guidelines, without losing sensitivity. An online calculator is available at www.melanomarisk.org.au .
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Uddin Ahmed Zihan, Zia, Mostafa A. Elseifi, Kevin Gaspard, and Zhongjie Zhang. "Development of a Structural Capacity Prediction Model Based on Traffic Speed Deflectometer Measurements." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 40 (April 23, 2018): 315–25. http://dx.doi.org/10.1177/0361198118758058.
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The importance of incorporating pavement structural conditions in the selection of maintenance and rehabilitation strategies along with functional indices has been recognized by state agencies. To measure in-service pavement structural capacity, surface deflection under a defined load has been typically used. The traffic speed deflectometer (TSD) has emerged as a continuous deflection-measuring device as it operates at traffic speed and reduces lane closure and user delays. The present study developed a nonlinear regression model to predict pavement structural number (SN) based on surface deflections measured by the TSD along with the total pavement layer thickness and traffic volume. The proposed model was successfully developed and validated with SN calculated based on TSD and falling weight deflectometer deflection data obtained from two testing programs in Louisiana and Idaho. The model was further validated with respect to its prediction of in-service structural capacity loss and deficiency. Based on statistical measures and the model’s ability in identifying structurally deficient sections, results showed satisfactory accuracy of the model and supports its use for network-level decision-making processes in the pavement management system.
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Natesan, Sathishkumar, and Rajakumar Krishnan. "FLCEER." International Journal of Information Technology and Web Engineering 15, no. 3 (July 2020): 76–101. http://dx.doi.org/10.4018/ijitwe.2020070105.
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Underwater acoustic sensor networks (UASN) play a crucial role in various applications such as tsunami detection, surveillance of the ocean by the defense department, monitoring offshore oil, and identifying gas basins underwater. UASNs can be one of the supporting infrastructures for the Internet of Things (IoT). UASNs have the problems of long latency, high bit error rate, and low bandwidth. These pose various challenges such as high consumption of energy, low reliability, low packet retransmission, and high delay for UASNs. To overcome the shortcomings mentioned above, various approaches are suggested. This article proposes a multi-layer fuzzy logic cluster-based energy-efficient routing protocol for UASNs. It splits the network area into equal sized rings. The priority number (PRN) is utilized for all underwater cluster heads (UCHs). Based on the highest PRN, the UCH starts communicating among UCHs. Here, the PRN makes the task very selective avoiding collisions and also reducing propagation delays. The cluster formation is done by sending a message to all underwater cluster members (UCMs) and the selection of UCH and UCM are done. Each has a threshold value. The intra-ring clustering process splits a ring into equal-sized clusters. Additionally, inter-cluster routing applies the fuzzy logic metrics to choose the optimum data route in transferring the data from the underwater cluster head (UCH) to the sink node (SN). It is tested using Aqua-Sim simulation which is based on NS2. It is compared with an existing protocol such as multi-layer cluster energy efficient (MLCEE), depth-based routing (DBR), energy efficient DBR (EEDBR). The results prove that it has improved energy efficiency, packet delivery ratio, throughput, and the network's lifetime.
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Balister, Paul, Béla Bollobás, Amites Sarkar, and Mark Walters. "Sentry Selection in Wireless Networks." Advances in Applied Probability 42, no. 1 (March 2010): 1–25. http://dx.doi.org/10.1239/aap/1269611141.
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Let be a Poisson process of intensity one in the infinite plane ℝ2. We surround each point x of by the open disc of radius r centred at x. Now let Sn be a fixed disc of area n, and let Cr(Sn) be the set of discs which intersect Sn. Write Erk for the event that Cr(Sn) is a k-cover of Sn, and Frk for the event that Cr(Sn) may be partitioned into k disjoint single covers of Sn. We prove that P(Erk ∖ Frk) ≤ ck / logn, and that this result is best possible. We also give improved estimates for P(Erk). Finally, we study the obstructions to k-partitionability in more detail. As part of this study, we prove a classification theorem for (deterministic) covers of ℝ2 with half-planes that cannot be partitioned into two single covers.
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Eltagy, Louay, Alex O'Neill-Kerr, and Nadia Hristova. "Using Qualitative-Electroencephalogram (Q-EEG) Mapping to Aid the Selection of Suitable Areas to Target Repetitive Transcranial Magnetic Stimulation (rTMS) Treatment in a Case of Depression With Comorbid Obsessive Compulsive Disorder (OCD)." BJPsych Open 8, S1 (June 2022): S4. http://dx.doi.org/10.1192/bjo.2022.82.
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AimsWe present the case of SN, a 25-year-old woman with diagnosis of anorexia nervosa, OCD, Generalized Anxiety Disorder (GAD) and depression. She has extensive history of contact with mental health services spanning more than 10 years. She has had 1 inpatient stay in an eating disorders unit lasting more than 6 months. Her treatment included various classes of medications, psychological therapy and social prescribing with little or no benefit. She has been referred to rTMS. The aims of the study are to determine the effect of rTMS in treatment of a patient with depression comorbid with OCD, understand the value of q-EEG in rTMS treatment and to treat OCD symptoms using rTMS guided by QEEG.MethodsSN had a total of 56 rTMS sessions targeting standard depression and anxiety areas; F3 (left sided excitatory) and F4 (right sided inhibitory). Following this her depression and anxiety improved but her OCD worsened. She then underwent a Q-EEG to be able to understand the physiological cause of her symptoms and suggest meaningful further neuromodulation that is tailored to her. This indicated dysregulation within the default mode network. Spindling beta waves were detected over the posterior electrode suggesting a tendency towards ruminations. There was clear hyperactivity in the supplementary motor area. SN had further 30 rTMS sessions targeting the OCD circuit (FC1 and FC2).ResultsRating scales showed a reduction in Patient Health Questionnaire-9 (PHQ-9) score from 22 to 14 (36%) in second course compared to an increase of PHQ-9 score from 9 to 15 (66.6%) in first course; indicating an overall 102% improvement in PHQ-9. It also showed reduction of Yale-Brown Obsessive Compulsive Scale (Y-BOCS) in second course from 34 to 8. It was not done in the first course but there was a clinical increase in OCD symptoms following the end of the first course. These results were corroborated clinically.A repeat q-EEG showed that the areas previously highlighted in red at FC1 and FC2 had now all reverted to green, indicating normal neuronal connectivity.ConclusionrTMS can provide timely and adequate response to depression and anxiety especially one that has not responded adequately to medications and psychotherapy. Q-EEG is useful to direct the plan, create a personalized plan and achieve accurate results. The use of q-EEG, whilst useful, should be balanced with other considerations as financial constraints. It should be reserved to patients who have not responded favorably to standard rTMS treatment.
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ZAWODNIAK, Józef J., Wiesław PIEKARSKI, and Mirosław GLISZCZYŃSKI. "Principles of Selection Surge Protection Device (SPD) in Medium Voltage Networks." AUTOMATYKA, ELEKTRYKA, ZAKLOCENIA 11, no. 1(39)2020 (March 31, 2020): 44–53. http://dx.doi.org/10.17274/aez.2020.39.03.
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Ren, Qian, and Guangshun Yao. "An Energy-Efficient Cluster Head Selection Scheme for Energy-Harvesting Wireless Sensor Networks." Sensors 20, no. 1 (December 28, 2019): 187. http://dx.doi.org/10.3390/s20010187.
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Concerning the large amount of energy consumption during the cluster head selection stage and the unequal harvested energy among nodes in energy-harvesting wireless sensor networks (EH-WSNs), an energy- efficient cluster head selection scheme called EECHS is proposed in this paper. The scheme divides all nodes from one cluster into three types: cluster head (CH), cluster member (CM), and scheduling node (SN). The SN is designed to monitor and store real-time information about the residual energy of all nodes, including CMs and the CH, in the same cluster. In the CH selection stage, the SN specifies a corresponding CM as the new CH according to the monitored results, thereby reducing the energy consumption caused by CH selection. In this way, the task of CH selection is migrated from CHs to SNs and, thus, the CHs can preserve more energy for data forwarding. Moreover, the EECHS adjusts the transmission radius of some nodes dynamically to prevent these nodes from discarding the harvested energy if their batteries are fully charged. A series of experiments were conducted to verify the effectiveness of the proposed EECHS, and the results demonstrate that EECHS can provide an efficient CH selection scheme for EH-WSNs and is able to use the harvested energy more efficiently than corresponding competitors.
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Ali, Syed Asif, Mubashar Sarfraz, Sajjad A. Ghauri, Asad Mahmood, Shahid Basir, Teweldebrhan Mezgebo Kebedew, and Sheraz Alam. "A Weighted Cluster Head Selection Algorithm for Energy Efficient Wireless Sensor Networks." Journal of Sensors 2022 (May 6, 2022): 1–13. http://dx.doi.org/10.1155/2022/3055178.
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The wireless sensor network’s (WSNs) lifetime is mainly dependent on the RE of the sensor nodes (SeN). In recent years, energy minimization in a WSN has been a prominent research topic, and numerous solutions have been proposed. This research focuses on the energy minimization of the SeNs where firstly, K-medoid clustering algorithm is applied to create clusters. Second, a weighted cluster head selection technique is used to choose a cluster head (CH) by integrating three independent weights associated with an SeN: energy, distance from the centroid, and distance from the sink node (SN). According to the energy level and distance from the SN and cluster’s centre, each node is assigned a constant weight. The simulation results are compared to existing methodologies, and the results show that the suggested network’s lifetime enhances.
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Jain, Khushboo, Anoop Kumar, and Vaibhav Vyas. "A Resilient Steady Clustering Technique for Sensor Networks." International Journal of Applied Evolutionary Computation 11, no. 4 (October 2020): 1–12. http://dx.doi.org/10.4018/ijaec.2020100101.
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In wireless sensor networks (WSNs), each sensor node is proficient to transmit data packets dynamically deprived of any constraint of fixed infrastructure. Sensor nodes (SNs) intermittently travels within the network from one cluster to another, which makes the network topology unsteady, uncertain, and unreliable. Consequently, it turns to be an immense challenge to sustain network stability and durability. In this work, the authors have presented a resilient steady clustering technique (RSCT) that will maintain durability and steadiness to the sensor network by reducing the unnecessary and avoidable cluster head (CH) changes and minimizing clustering and networking overheads. In the presented technique, they have introduced a new SN that acts as a standby node (SBN) in the cluster. This SBN performs the tasks of CH whenever the actual CH moves from the cluster. Later the CH re-elect the new SBN. This process keeps the network available and serviceable without any interruption. The decision for selecting the CH and SBN depends on the optimal CH threshold function and an energy threshold function.
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Xiao, Xingxing, Haining Huang, and Wei Wang. "Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms." Applied Sciences 11, no. 1 (December 30, 2020): 312. http://dx.doi.org/10.3390/app11010312.
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Due to the limited battery energy of underwater wireless sensor nodes and the difficulty in replacing or recharging the battery underwater, it is of great significance to improve the energy efficiency of underwater wireless sensor networks (UWSNs). We propose a novel energy-efficient clustering routing protocol based on data fusion and genetic algorithms (GAs) for UWSNs. In the clustering routing protocol, the cluster head node (CHN) gathers the data from cluster member nodes (CMNs), aggregates the data through an improved back propagation neural network (BPNN), and transmits the aggregated data to a sink node (SN) through a multi-hop scheme. The effective multi-hop transmission path between the CHN and the SN is determined through the enhanced GA, thereby improving transmission efficiency and reducing energy consumption. This paper presents the GA based on a specific encoding scheme, a particular crossover operation, and an enhanced mutation operation. Additionally, the BPNN employed for data fusion is improved by adopting an optimized momentum method, which can reduce energy consumption through the elimination of data redundancy and the decrease of the amount of transferred data. Moreover, we introduce an optimized CHN selecting scheme considering residual energy and positions of nodes. The experiments demonstrate that our proposed protocol outperforms its competitors in terms of the energy expenditure, the network lifespan, and the packet loss rate.
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Stahl, Benjamin E., Jorge Martínez-Palomera, WeiKang Zheng, Thomas de Jaeger, Alexei V. Filippenko, and Joshua S. Bloom. "deepSIP: linking Type Ia supernova spectra to photometric quantities with deep learning." Monthly Notices of the Royal Astronomical Society 496, no. 3 (June 17, 2020): 3553–71. http://dx.doi.org/10.1093/mnras/staa1706.
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ABSTRACT We present deepSIP (deep learning of Supernova Ia Parameters), a software package for measuring the phase and – for the first time using deep learning – the light-curve shape of a Type Ia supernova (SN Ia) from an optical spectrum. At its core, deepSIP consists of three convolutional neural networks trained on a substantial fraction of all publicly available low-redshift SN Ia optical spectra, on to which we have carefully coupled photometrically derived quantities. We describe the accumulation of our spectroscopic and photometric data sets, the cuts taken to ensure quality, and our standardized technique for fitting light curves. These considerations yield a compilation of 2754 spectra with photometrically characterized phases and light-curve shapes. Though such a sample is significant in the SN community, it is small by deep-learning standards where networks routinely have millions or even billions of free parameters. We therefore introduce a data-augmentation strategy that meaningfully increases the size of the subset we allocate for training while prioritizing model robustness and telescope agnosticism. We demonstrate the effectiveness of our models by deploying them on a sample unseen during training and hyperparameter selection, finding that Model I identifies spectra that have a phase between −10 and 18 d and light-curve shape, parametrized by Δm15, between 0.85 and 1.55 mag with an accuracy of 94.6 per cent. For those spectra that do fall within the aforementioned region in phase–Δm15 space, Model II predicts phases with a root-mean-square error (RMSE) of 1.00 d and Model III predicts Δm15 values with an RMSE of 0.068 mag.
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Shen, Shigen, Longjun Huang, En Fan, Keli Hu, Jianhua Liu, and Qiying Cao. "Trust Dynamics in WSNs: An Evolutionary Game-Theoretic Approach." Journal of Sensors 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4254701.
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A sensor node (SN) in Wireless Sensor Networks (WSNs) can decide whether to collaborate with others based on a trust management system (TMS) by making a trust decision. In this paper, we study the trust decision and its dynamics that play a key role to stabilize the whole network using evolutionary game theory. When SNs are making their decisions to select actionTrustorMistrust, a WSNs trust game is created to reflect their utilities. An incentive mechanism bound with one SN’s trust degree is incorporated into this trust game and effectively promotes SNs to select actionTrust. The replicator dynamics of SNs’ trust evolution, illustrating the evolutionary process of SNs selecting their actions, are given. We then propose and prove the theorems indicating that evolutionarily stable strategies can be attained under different parameter values, which supply theoretical foundations to devise a TMS for WSNs. Moreover, we can find out the conditions that will lead SNs to choose actionTrustas their final behavior. In this manner, we can assure WSNs’ security and stability by introducing a trust mechanism to satisfy these conditions. Experimental results have confirmed the proposed theorems and the effects of the incentive mechanism.
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Huang, Xin, Yimin Wang, Peiqun Lin, Heng Yu, and Yue Luo. "Forecasting the All-Weather Short-Term Metro Passenger Flow Based on Seasonal and Nonlinear LSSVM." Promet - Traffic&Transportation 33, no. 2 (March 30, 2021): 217–31. http://dx.doi.org/10.7307/ptt.v33i2.3561.
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Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term passenger flow prediction needs to consider many factors, and the results of the existing models for short-term subway passenger flow forecasting are often unsatisfactory. Along this line, we propose a parallel architecture, called the seasonal and nonlinear least squares support vector machine (SN-LSSVM), to extract the periodicity and nonlinearity characteristics of passenger flow. Various forecasting models, including auto-regressive integrated moving average, long short-term memory network, and support vector machine, are employed for evaluating the performance of the proposed architecture. Moreover, we first applied the method to the Tiyu Xilu station which is the most crowded station in the Guangzhou metro. The results indicate that the proposed model can effectively make all-weather and year-round passenger flow predictions, thus contributing to the management of the station.
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Tošic, Aleksandar, and Jernej Vičič. "Spatial Path Selection and Network Topology Optimisation in P2P Anonymous Routing Protocols." Journal of Web Engineering, November 28, 2021. http://dx.doi.org/10.13052/jwe1540-9589.2115.
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To anonymous internet traffic, many popular protocols route traffic through a network of nodes in order to conceal information about the request. However, routing traffic through other nodes inherently introduces added latency. Over the past two decades, there were many attempts to improve the path selection in order to decrease latency with little or no trade-off in terms of security, and anonymity. In this paper, we show the potential use of geo-sharding in decentralized routing networks to improve fault-tolerance, and latency. Such networks can be used as a communication layer for Edge devices computing huge amounts of data. Specifically, we focus our work on Low Latency Anonymous Routing Protocol (LLARP), a protocol built on top of Oxen blockchain that aims to achieve internet privacy. We analyse the existing network of Service Nodes(SN), observe cloud provider centralisation, and propose a high level protocol that provides incentives for a better geographical distribution mitigating potential cloud provider/country wide service dropouts. Additionally, the protocol level information about geographical location can be used to improve client’s path (the string of nodes that will participate in the transaction) selection, decreasing network latency. We show the feasibility of our approach by comparing it with the random path selection in a simulated environment. We observe marginal drops in average latency when selecting paths geographically closer to each other.
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Zhang, Gexiang, Xihai Zhang, Haina Rong, Prithwineel Paul, Ming Zhu, Ferrante Neri, and Yew-Soon Ong. "A Layered Spiking Neural System for Classification Problems." International Journal of Neural Systems, April 12, 2022. http://dx.doi.org/10.1142/s012906572250023x.
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Biological brains have a natural capacity for resolving certain classification tasks. Studies on biologically plausible spiking neurons, architectures and mechanisms of artificial neural systems that closely match biological observations while giving high classification performance are gaining momentum. Spiking neural P systems (SN P systems) are a class of membrane computing models and third-generation neural networks that are based on the behavior of biological neural cells and have been used in various engineering applications. Furthermore, SN P systems are characterized by a highly flexible structure that enables the design of a machine learning algorithm by mimicking the structure and behavior of biological cells without the over-simplification present in neural networks. Based on this aspect, this paper proposes a novel type of SN P system, namely, layered SN P system (LSN P system), to solve classification problems by supervised learning. The proposed LSN P system consists of a multi-layer network containing multiple weighted fuzzy SN P systems with adaptive weight adjustment rules. The proposed system employs specific ascending dimension techniques and a selection method of output neurons for classification problems. The experimental results obtained using benchmark datasets from the UCI machine learning repository and MNIST dataset demonstrated the feasibility and effectiveness of the proposed LSN P system. More importantly, the proposed LSN P system presents the first SN P system that demonstrates sufficient performance for use in addressing real-world classification problems.
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Liddell, Belinda J., Pritha Das, Gin S. Malhi, Kim L. Felmingham, Tim Outhred, Jessica Cheung, Miriam Den, et al. "Torture exposure and the functional brain: investigating disruptions to intrinsic network connectivity using resting state fMRI." Translational Psychiatry 12, no. 1 (January 26, 2022). http://dx.doi.org/10.1038/s41398-022-01795-3.
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AbstractTorture has profound psychological and physiological consequences for survivors. While some brain structures and functions appear altered in torture survivors, it is unclear how torture exposure influences functional connectivity within and between core intrinsic brain networks. In this study, 37 torture survivors (TS) and 62 non-torture survivors (NTS) participated in a resting-state fMRI scan. Data-driven independent components analysis identified active intrinsic networks. Group differences in functional connectivity in the default mode network (DMN), salience network (SN) and central executive network (CEN) of the triple network model, as well any prefrontal network, were examined while controlling for PTSD symptoms and exposure to other potentially traumatic events. The analysis identified 25 networks; eight comprised our networks of interest. Within-network group differences were observed in the left CEN (lCEN), where the TS group showed less spectral power in the low-frequency band. Differential internetwork dynamic connectivity patterns were observed, where the TS group showed stronger positive coupling between the lCEN and anterior dorsomedial and ventromedial DMN, and stronger negative coupling between a lateral frontal network and the lCEN and anterior dorsomedial DMN (when contrasted with the NTS group). Group differences were not attributed to torture severity or dissociative symptoms. Torture survivors showed disrupted dynamic functional connectivity between a laterally-aligned lCEN that serves top-down control functions over external processes and the midline DMN that underpins internal self-referential processes, which may be an adaptive response to mitigate the worst effects of the torture experience. This study provides a critical step in mapping the neural signature of torture exposure to guide treatment development and selection.
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Zhao, Weiwei, Yida Wang, Fangfang Zhou, Gaiying Li, Zhichao Wang, Haodong Zhong, Yang Song, et al. "Automated Segmentation of Midbrain Structures in High-Resolution Susceptibility Maps Based on Convolutional Neural Network and Transfer Learning." Frontiers in Neuroscience 16 (February 10, 2022). http://dx.doi.org/10.3389/fnins.2022.801618.
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BackgroundAccurate delineation of the midbrain nuclei, the red nucleus (RN), substantia nigra (SN) and subthalamic nucleus (STN), is important in neuroimaging studies of neurodegenerative and other diseases. This study aims to segment midbrain structures in high-resolution susceptibility maps using a method based on a convolutional neural network (CNN).MethodsThe susceptibility maps of 75 subjects were acquired with a voxel size of 0.83 × 0.83 × 0.80 mm3 on a 3T MRI system to distinguish the RN, SN, and STN. A deeply supervised attention U-net was pre-trained with a dataset of 100 subjects containing susceptibility maps with a voxel size of 0.63 × 0.63 × 2.00 mm3 to provide initial weights for the target network. Five-fold cross-validation over the training cohort was used for all the models’ training and selection. The same test cohort was used for the final evaluation of all the models. Dice coefficients were used to assess spatial overlap agreement between manual delineations (ground truth) and automated segmentation. Volume and magnetic susceptibility values in the nuclei extracted with automated CNN delineation were compared to those extracted by manual tracing. Consistencies of volume and magnetic susceptibility values by different extraction strategies were assessed by Pearson correlation coefficients and Bland-Altman analyses.ResultsThe automated CNN segmentation method achieved mean Dice scores of 0.903, 0.864, and 0.777 for the RN, SN, and STN, respectively. There were no significant differences between the achieved Dice scores and the inter-rater Dice scores (p > 0.05 for each nucleus). The overall volume and magnetic susceptibility values of the nuclei extracted by the automatic CNN method were significantly correlated with those by manual delineation (p < 0.01).ConclusionMidbrain structures can be precisely segmented in high-resolution susceptibility maps using a CNN-based method.
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Cyriac, Robin, and Saleem Durai M.A. "LMH-RPL: a load balancing and mobility aware secure hybrid routing protocol for low power lossy network." International Journal of Pervasive Computing and Communications, September 20, 2022. http://dx.doi.org/10.1108/ijpcc-05-2022-0213.
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Purpose Routing protocol for low-power lossy network (RPL) being the de facto routing protocol used by low power lossy networks needs to provide adequate routing service to mobile nodes (MNs) in the network. As RPL is designed to work under constraint power requirements, its route updating frequency is not sufficient for MNs in the network. The purpose of this study is to ensure that MNs enjoy seamless connection throughout the network with minimal handover delay. Design/methodology/approach This study proposes a load balancing mobility aware secure hybrid – RPL in which static node (SN) identifies route using metrics like expected transmission count, and path delay and parent selection are further refined by working on remaining energy for identifying the primary route and queue availability for secondary route maintenance. MNs identify route with the help of smart timers and by using received signal strength indicator sampling of parent and neighbor nodes. In this work, MNs are also secured against rank attack in RPL. Findings This model produces favorable result in terms of packet delivery ratio, delay, energy consumption and number of living nodes in the network when compared with different RPL protocols with mobility support. The proposed model reduces packet retransmission in the network by a large margin by providing load balancing to SNs and seamless connection to MNs. Originality/value In this work, a novel algorithm was developed to provide seamless handover for MNs in network. Suitable technique was developed to provide load balancing to SNs in network by maintaining appropriate secondary route.
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Menichelli, Irene, Pasquale De Gori, Francesco Pio Lucente, Luigi Improta, Luisa Valoroso, Paola Baccheschi, Samer Bagh, Caterina Montuori, and Claudio Chiarabba. "Minimum 1D VP and VP/VS Models and Hypocentral Determinations in the Central Mediterranean Area." Seismological Research Letters, June 1, 2022. http://dx.doi.org/10.1785/0220220079.
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Abstract Minimum 1D velocity models and station corrections have been computed for the central Mediterranean area using two main data sets. The first one consists of accurate first arrival-time readings from 103 seismic events with magnitude (ML)≥3.5 recorded by the Italian National Seismic Network (RSN) and the AlpArray Seismic Network (AASN) in the period 2014–2021. Earthquakes were selected on the basis of their spatial distribution, epicentral distance to the nearest seismic station, and maximum distance traveled by Pn and Sn phases. This fine selection of high-quality data combined with the spatial density of the AlpArray seismic stations was decisive in obtaining high resolution for upper mantle velocity, especially in the Alpine belt. To obtain a denser coverage of crustal rays, we extended the first data set with P and S arrivals of local earthquakes from Istituto Nazionale di Geofisica e Vulcanologia (INGV) bulletin data (2016–2018). A total of 75,807 seismic phases (47,183 P phases and 28,264 S phases) have been inverted to calculate best-fit 1D velocity models, at regional and local scales. We then test the performance of the optimized velocity models by relocating the last four years of seismicity recorded by INGV (period 2017–2020). The computed velocity models are very effective for routine earthquake location, seismic monitoring, source parameter modeling, and future 3D seismic tomography.
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Menichelli, Irene, Pasquale De Gori, Francesco Pio Lucente, Luigi Improta, Luisa Valoroso, Paola Baccheschi, Samer Bagh, Caterina Montuori, and Claudio Chiarabba. "Minimum 1D VP and VP/VS Models and Hypocentral Determinations in the Central Mediterranean Area." Seismological Research Letters, June 1, 2022. http://dx.doi.org/10.1785/0220220079.
Full textAbstract:
Abstract Minimum 1D velocity models and station corrections have been computed for the central Mediterranean area using two main data sets. The first one consists of accurate first arrival-time readings from 103 seismic events with magnitude (ML)≥3.5 recorded by the Italian National Seismic Network (RSN) and the AlpArray Seismic Network (AASN) in the period 2014–2021. Earthquakes were selected on the basis of their spatial distribution, epicentral distance to the nearest seismic station, and maximum distance traveled by Pn and Sn phases. This fine selection of high-quality data combined with the spatial density of the AlpArray seismic stations was decisive in obtaining high resolution for upper mantle velocity, especially in the Alpine belt. To obtain a denser coverage of crustal rays, we extended the first data set with P and S arrivals of local earthquakes from Istituto Nazionale di Geofisica e Vulcanologia (INGV) bulletin data (2016–2018). A total of 75,807 seismic phases (47,183 P phases and 28,264 S phases) have been inverted to calculate best-fit 1D velocity models, at regional and local scales. We then test the performance of the optimized velocity models by relocating the last four years of seismicity recorded by INGV (period 2017–2020). The computed velocity models are very effective for routine earthquake location, seismic monitoring, source parameter modeling, and future 3D seismic tomography.
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Karchev, Konstantin, Roberto Trotta, and Christoph Weniger. "SICRET: Supernova Ia cosmology with truncated marginal neural Ratio EsTimation." Monthly Notices of the Royal Astronomical Society, December 30, 2022. http://dx.doi.org/10.1093/mnras/stac3785.
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Abstract Type Ia supernovæ (SNæ Ia), standardisable candles that allow tracing the expansion history of the Universe, are instrumental in constraining cosmological parameters, particularly dark energy. State-of-the-art likelihood-based analyses scale poorly to future large datasets, are limited to simplified probabilistic descriptions, and must explicitly sample a high-dimensional latent posterior to infer the few parameters of interest, which makes them inefficient. Marginal likelihood-free inference, on the other hand, is based on forward simulations of data, and thus can fully account for complicated redshift uncertainties, contamination from non-SN Ia sources, selection effects, and a realistic instrumental model. All latent parameters, including instrumental and survey-related ones, per-object and population-level properties, are implicitly marginalised, while the cosmological parameters of interest are inferred directly. As a proof of concept, we apply truncated marginal neural ratio estimation (TMNRE), a form of marginal likelihood-free inference, to Bahamas, a Bayesian hierarchical model for salt parameters. We verify that TMNRE produces unbiased and precise posteriors for cosmological parameters from up to 100 000 SNæ Ia. With minimal additional effort, we train a network to infer simultaneously the ${\cal O}{{100\, 000}}$ latent parameters of the supernovæ (e.g. absolute brightnesses). In addition, we describe and apply a procedure that utilises local amortisation of the inference to convert the approximate Bayesian posteriors into frequentist confidence regions with exact coverage. Finally, we discuss the planned improvements to the model that are enabled by using a likelihood-free inference framework, like selection effects and non-Ia contamination.
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Wen, Qiao, Peihong Ma, Xiaohui Dong, Ruirui Sun, Lei Lan, Tao Yin, Yuzhu Qu, Yalan Liu, Qingqing Xiao, and Fang Zeng. "Neuroimaging Studies of Acupuncture on Low Back Pain: A Systematic Review." Frontiers in Neuroscience 15 (September 20, 2021). http://dx.doi.org/10.3389/fnins.2021.730322.
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Objectives: This study was conducted in order to investigate the study design and main outcomes of acupuncture neuroimaging studies on low back pain (LBP).Methods: Neuroimaging studies of acupuncture on LBP were collected from three English databases such as PubMed and four Chinese databases such as China National Knowledge Infrastructure (CNKI) from inception to December 31, 2020. Study selection, data extraction, and assessment of risk of bias were performed independently by two investigators. The quality of studies was appraised with the Cochrane's risk of bias tools. Information on basic information, methodology, and brain imaging data were extracted.Results: The literature search returned 310 potentially eligible studies and 19 articles met inclusion criteria; 78.9% of studies chose manual acupuncture as the intervention, 89.5% of studies evaluated functional changes elicited by acupuncture, and 68.4% of studies used resting-state fMRI as imaging condition. The most frequently reported acupuncture-induced brain alterations of LBP patients were in the prefrontal cortex, insula, cerebellum, primary somatosensory cortex, and anterior cingulate cortex. There was a significant correlation between improved clinical outcomes and changes in the brain.Conclusions: The results suggested that improving abnormal structure and functional activities in the brain of the LBP patient is an important mechanism of acupuncture treatment for LBP. The brain regions involved in acupuncture analgesia for LBP were mainly located in the pain matrix, default mode network (DMN), salience network (SN), and descending pain modulatory system (DPMS). However, it was difficult to draw a generalized conclusion due to the heterogeneity of study designs. Further well-designed multimodal neuroimaging studies investigating the mechanism of acupuncture on LBP are warranted.
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Zhang, Xi, Zhihua Shao, Sutong Xu, Qiulu Liu, Chenming Liu, Yuping Luo, Lingjing Jin, and Siguang Li. "Immune Profiling of Parkinson’s Disease Revealed Its Association With a Subset of Infiltrating Cells and Signature Genes." Frontiers in Aging Neuroscience 13 (February 9, 2021). http://dx.doi.org/10.3389/fnagi.2021.605970.
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Parkinson’s disease (PD) is an age-related and second most common neurodegenerative disorder. In recent years, increasing evidence revealed that peripheral immune cells might be able to infiltrate into brain tissues, which could arouse neuroinflammation and aggravate neurodegeneration. This study aimed to illuminate the landscape of peripheral immune cells and signature genes associated with immune infiltration in PD. Several transcriptomic datasets of substantia nigra (SN) from the Gene Expression Omnibus (GEO) database were separately collected as training cohort, testing cohort, and external validation cohort. The immunoscore of each sample calculated by single-sample gene set enrichment analysis was used to reflect the peripheral immune cell infiltration and to identify the differential immune cell types between PD and healthy participants. According to receiver operating characteristic (ROC) curve analysis, the immunoscore achieved an overall accuracy of the area under the curve (AUC) = 0.883 in the testing cohort, respectively. The immunoscore displayed good performance in the external validation cohort with an AUC of 0.745. The correlation analysis and logistic regression analysis were used to analyze the correlation between immune cells and PD, and mast cell was identified most associated with the occurrence of PD. Additionally, increased mast cells were also observed in our in vivo PD model. Weighted gene co-expression network analysis (WGCNA) was used to selected module genes related to a mast cell. The least absolute shrinkage and selection operator (LASSO) analysis and random-forest analysis were used to analyze module genes, and two hub genes RBM3 and AGTR1 were identified as associated with mast cells in the training cohort. The expression levels of RBM3 and AGTR1 in these cohorts and PD models revealed that these hub genes were significantly downregulated in PD. Moreover, the expression trend of the aforementioned two genes differed in mast cells and dopaminergic (DA) neurons. In conclusion, this study not only exhibited a landscape of immune infiltrating patterns in PD but also identified mast cells and two hub genes associated with the occurrence of PD, which provided potential therapeutic targets for PD patients (PDs).
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Khatri, Uttam, and Goo-Rak Kwon. "Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI." Frontiers in Aging Neuroscience 14 (May 30, 2022). http://dx.doi.org/10.3389/fnagi.2022.818871.
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Accurate diagnosis of the initial phase of Alzheimer’s disease (AD) is essential and crucial. The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). So far, several anatomical MRI imaging markers for AD diagnosis have been identified. The use of cortical and subcortical volumes, the hippocampus, and amygdala volume, as well as genetic patterns, has proven to be beneficial in distinguishing patients with AD from the healthy population. The fMRI time series data have the potential for specific numerical information as well as dynamic temporal information. Voxel and graphical analyses have gained popularity for analyzing neurodegenerative diseases, such as Alzheimer’s and its prodromal phase, mild cognitive impairment (MCI). So far, these approaches have been utilized separately for the diagnosis of AD. In recent studies, the classification of cases of MCI into those that are not converted for a certain period as stable MCI (MCIs) and those that converted to AD as MCIc has been less commonly reported with inconsistent results. In this study, we verified and validated the potency of a proposed diagnostic framework to identify AD and differentiate MCIs from MCIc by utilizing the efficient biomarkers obtained from sMRI, along with functional brain networks of the frequency range .01–.027 at the resting state and the voxel-based features. The latter mainly included default mode networks (amplitude of low-frequency fluctuation [ALFF], fractional ALFF [ALFF], and regional homogeneity [ReHo]), degree centrality (DC), and salience networks (SN). Pearson’s correlation coefficient for measuring fMRI functional networks has proven to be an efficient means for disease diagnosis. We applied the graph theory to calculate nodal features (nodal degree [ND], nodal path length [NL], and between centrality [BC]) as a graphical feature and analyzed the connectivity link between different brain regions. We extracted three-dimensional (3D) patterns to calculate regional coherence and then implement a univariate statistical t-test to access a 3D mask that preserves voxels showing significant changes. Similarly, from sMRI, we calculated the hippocampal subfield and amygdala nuclei volume using Freesurfer (version 6). Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. We also compared the performance of SVM with Random Forest (RF) classifiers. The obtained results demonstrated the potency of our framework, wherein a combination of the hippocampal subfield, the amygdala volume, and brain networks with multiple measures of rs-fMRI could significantly enhance the accuracy of other approaches in diagnosing AD. The accuracy obtained by the proposed method was reported for binary classification. More importantly, the classification results of the less commonly reported MCIs vs. MCIc improved significantly. However, this research involved only the AD Neuroimaging Initiative (ADNI) cohort to focus on the diagnosis of AD advancement by integrating sMRI and fMRI. Hence, the study’s primary disadvantage is its small sample size. In this case, the dataset we utilized did not fully reflect the whole population. As a result, we cannot guarantee that our findings will be applicable to other populations.
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"Best Paper Selection." Yearbook of Medical Informatics 28, no. 01 (August 2019): 206–7. http://dx.doi.org/10.1055/s-0039-1677922.
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Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W. Federated learning of predictive models from federated Electronic Health Records. Int J Med Inform 2018 Apr;112:59-67 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5836813/ Daniel C, Serre P, Orlova N, Bréant S, Paris N, Griffon N. Initializing a hospital-wide data quality program. The AP-HP experience. Comput Methods Programs Biomed 2018 Nov 9 https://www.sciencedirect.com/science/article/pii/S0169260718306242?via%3Dihub Estiri H, Stephens KA, Klann JG, Murphy SN. Exploring completeness in clinical data research networks with DQe-c. J Am Med Inform Assoc 2018 Jan 1;25(1):17-24 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6481389/ Sylvestre E, Bouzillé G, Chazard E, His-Mahier C, Riou C, Cuggia M. Combining information from a clinical data warehouse and a pharmaceutical database to generate a framework to detect comorbidities in electronic health records. BMC Med Inform Decis Mak 2018 Jan 24;18(1):9 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5784648/
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Bilek, Furkan, Ferhat Balgetir, Caner Feyzi Demir, Gökhan Alkan, and Seda Arslan-Tuncer. "Quantitative Assessment of Ataxia in Multiple Sclerosis Patients using Spatiotemporal Parameters: A Relief-Based Machine Learning Analysis." Physikalische Medizin, Rehabilitationsmedizin, Kurortmedizin, August 23, 2021. http://dx.doi.org/10.1055/a-1512-4858.
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Abstract Background and Objective Multiple sclerosis (MS) is a chronic, progressive, and autoimmune disease of the central nervous system (CNS) characterized by inflammation, demyelination, and axonal injury. In patients with newly diagnosed MS (ndMS), ataxia can present either as mild or severe and can be difficult to diagnose in the absence of clinical disability. Such difficulties can be eliminated by using decision support systems supported by machine learning methods. The present study aimed to achieve early diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. Materials and Methods The prospective study included 32 ndMS patients with an Expanded Disability Status Scale (EDSS) score of≤2.0 and 32 healthy volunteers. A total of 14 parameters were elicited by using a Win-Track platform. The ndMS patients were differentiated from healthy individuals using multiple classifiers including Artificial Neural Network (ANN), Support Vector Machine (SVM), the k-nearest neighbors (K-NN) algorithm, and Decision Tree Learning (DTL). To improve the performance of the classification, a Relief-based feature selection algorithm was applied to select the subset that best represented the whole dataset. Performance evaluation was achieved based on several criteria such as Accuracy (ACC), Sensitivity (SN), Specificity (SP), and Precision (PREC). Results ANN had a higher classification performance compared to other classifiers, whereby it provided an accuracy, sensitivity, and specificity of 89, 87.8, 90.3% with the use of all parameters and provided the values of 93.7, 96.6%, and 91.1% with the use of parameters selected by the Relief algorithm, respectively. Significance To our knowledge, this is the first study of its kind in the literature to investigate the diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. The proposed method, i. e. Relief-based ANN method, successfully diagnosed ataxia by using a lower number of parameters compared to the numbers of parameters reported in clinical studies, thereby reducing the costs and increasing the performance of the diagnosis. The method also provided higher rates of accuracy, sensitivity, and specificity in the diagnosis of ataxia in ndMS patients compared to other methods. Taken together, these findings indicate that the proposed method could be helpful in the diagnosis of ataxia in minimally impaired ndMS patients and could be a pathfinder for future studies.
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Arvanitaki, Antonia, Nikolaos Pappas, Niklas Carlsson, Parthajit Mohapatra, and Oleg Burdakov. "Performance analysis of congestion-aware secure broadcast channels." EURASIP Journal on Wireless Communications and Networking 2021, no. 1 (September 25, 2021). http://dx.doi.org/10.1186/s13638-021-02046-7.
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AbstractCongestion-aware scheduling in case of downlink cellular communication has ignored the distribution of diverse content to different clients with heterogeneous secrecy requirements. Other possible application areas that encounter the preceding issue are secure offloading in mobile-edge computing, and vehicular communication. In this paper, we extend the work in Arvanitaki et al. (SN Comput Sci 1(1):53, 2019) by taking into consideration congestion and random access. Specifically, we study a two-user congestion-aware broadcast channel with heterogeneous traffic and different security requirements. We consider two randomized policies for selecting which packets to transmit, one is congestion-aware by taking into consideration the queue size, whereas the other one is congestion-agnostic. We analyse the throughput and the delay performance under two decoding schemes at the receivers, and provide insights into their relative security performance and into how congestion control at the queue holding confidential information can help decrease the average delay per packet. We show that the congestion-aware policy provides better delay, throughput, and secrecy performance for large arrival packet probabilities at the queue holding the confidential information. The derived results also take account of the self-interference caused at the receiver for whom confidential data is intended due to its full-duplex operation while jamming the communication at the other user. Finally, for two decoding schemes, we formulate our problems in terms of multi-objective optimization, which allows for finding a trade-off between the average packet delay for packets intended for the legitimate user and the throughput for the other user under congestion-aware policy.