bluuebunny/crossref_metadata_2025_split huggingface dataset:
Flexible needle puncture path planning for liver tumors based on deep reinforcement learning
Authors: Hu Wenrui , Jiang Huiyan , Wang Meng 
Published: September 2022

Abstract

Objective. Minimally invasive surgery has been widely adopted in the treatment of patients with liver tumors. In liver tumor puncture surgery, an image-guided ablation needle for puncture surgery, which first reaches a target tumor along a predetermined path, and then ablates the tumor or injects drugs near the tumor, is often used to reduce patient trauma, improving the safety of surgery operations and avoiding possible damage to large blood vessels and key organs. In this paper, a path planning method for computer tomography (CT) guided ablation needle in liver tumor puncture surgery is proposed. Approach. Given a CT volume containing abdominal organs, we first classify voxels and optimize the number of voxels to reduce volume rendering pressure, then we reconstruct a multi-scale 3D model of the liver and hepatic vessels. Secondly, multiple entry points of the surgical path are selected based on the strong and weak constraints of clinical puncture surgery through multi-agent reinforcement learning. We select the optimal needle entry point based on the length measurement. Then, through the incremental training of the double deep Q-learning network (DDQN), the transmission of network parameters from the small-scale environment to the larger-scale environment is accomplished, and the optimal surgical path with more optimized details is obtained. Main results. To avoid falling into local optimum in network training, improve both the convergence speed and performance of the network, and maximize the cumulative reward, we train the path planning network on different scales 3D reconstructed organ models, and validate our method on tumor samples from public datasets. The scores of human surgeons verified the clinical relevance of the proposed method. Significance. Our method can robustly provide the optimal puncture path of flexible needle for liver tumors, which is expected to provide a reference for surgeons’ preoperative planning.
Signal processing and computational modeling for interpretation of SEEG-recorded interictal epileptiform discharges in epileptogenic and non-epileptogenic zones
Authors: Köksal-Ersöz Elif , Lazazzera Remo , Yochum Maxime , Merlet Isabelle , Makhalova Julia , Mercadal Borja , Sanchez-Todo Roser , Ruffini Giulio , Bartolomei Fabrice , Benquet Pascal , Wendling Fabrice 
Published: September 2022

Abstract

Objective. In partial epilepsies, interictal epileptiform discharges (IEDs) are paroxysmal events observed in epileptogenic zone (EZ) and non-epileptogenic zone (NEZ). IEDs’ generation and recurrence are subject to different hypotheses: they appear through glutamatergic and gamma-aminobutyric acidergic (GABAergic) processes; they may trigger seizures or prevent seizure propagation. This paper focuses on a specific class of IEDs, spike-waves (SWs), characterized by a short-duration spike followed by a longer duration wave, both of the same polarity. Signal analysis and neurophysiological mathematical models are used to interpret puzzling IED generation. Approach. Interictal activity was recorded by intracranial stereo-electroencephalography (SEEG) electrodes in five different patients. SEEG experts identified the epileptic and non-epileptic zones in which IEDs were detected. After quantifying spatial and temporal features of the detected IEDs, the most significant features for classifying epileptic and non-epileptic zones were determined. A neurophysiologically-plausible mathematical model was then introduced to simulate the IEDs and understand the underlying differences observed in epileptic and non-epileptic zone IEDs. Main results. Two classes of SWs were identified according to subtle differences in morphology and timing of the spike and wave component. Results showed that type-1 SWs were generated in epileptogenic regions also involved at seizure onset, while type-2 SWs were produced in the propagation or non-involved areas. The modeling study indicated that synaptic kinetics, cortical organization, and network interactions determined the morphology of the simulated SEEG signals. Modeling results suggested that the IED morphologies were linked to the degree of preserved inhibition. Significance. This work contributes to the understanding of different mechanisms generating IEDs in epileptic networks. The combination of signal analysis and computational models provides an efficient framework for exploring IEDs in partial epilepsies and classifying EZ and NEZ.
A portable neurostimulator circuit with anodic bias enhances stimulation injection capacity
Authors: Ersöz Alpaslan , Kim Insoo , Han Martin 
Published: September 2022

Abstract

Objective. Electrochemically safe and efficient charge injection for neural stimulation necessitates monitoring of polarization and enhanced charge injection capacity of the stimulating electrodes. In this work, we present improved microstimulation capability by developing a custom-designed multichannel portable neurostimulator with a fully programmable anodic bias circuitry and voltage transient monitoring feature. Approach. We developed a 16-channel multichannel neurostimulator system, compared charge injection capacities as a function of anodic bias potentials, and demonstrated convenient control of the system by a custom-designed user interface allowing bidirectional wireless data transmission of stimulation parameters and recorded voltage transients. Charge injections were conducted in phosphate-buffered saline with silicon-based iridium oxide microelectrodes. Main results. Under charge-balanced 200 µs cathodic first pulsing, the charge injection capacities increased proportionally to the level of anodic bias applied, reaching a maximum of ten-fold increase in current intensity from 10 µA (100 µC cm−2) to 100 µA (1000 µC cm−2) with a 600 mV anodic bias. Our custom-designed and completely portable 16-channel neurostimulator enabled a significant increase in charge injection capacity in vitro. Significance. Limited charge injection capacity has been a bottleneck in neural stimulation applications, and our system may enable efficacious behavioral animal study involving chronic microstimulation while ensuring electrochemical safety.
Enhancing historical electron temperature data with an artificial neural network in the C-2U FRC
Authors: Player G. , Magee R. M. , Tajima T. , Trask E. , Zhai K. 
Published: September 2022

Abstract
The electron temperature is a vital parameter in understanding the dynamics of fusion plasmas, helping to determine basic properties of the system, stability, and fast ion lifetime. We present a method for improving the sampling rate of historical Thomson scattering data by a factor of 103 on the decommissioned beam-driven C-2U field reversed configuration device by utilizing an artificial neural network. This work details the construction of the model, including an analysis of input signals and the model hyperparameter space. The model’s performance is evaluated on both a random subset and selected ensemble of testing data and its predictions are found to agree with the Thomson measurements in both cases. Finally, the model is used to reconstruct the effect of the micro-burst instability in C-2U, which is then compared to more recent results in C-2W, showing that the effects of the micro-burst on core electron temperature have been mitigated in C-2W.
Evaporation progress of macroscopic-scale droplets on heated substrates
Authors: Zhu Zhiqiang , Zhao Dongdong , Liu Qiusheng , Qin Jun , Tao Yuequn , He Naifeng , Xu Yongxiang 
Published: September 2022

Abstract
Evaporation progress of a macroscopic-scale sessile droplet with the pinned triple line on heated isothermal substrates has been experimentally investigated in the terrestrial gravity, in order to study the interface effect, heat and mass transfer behaviours during the phase change process. The experiments were carried out in a closed chamber in which the environment temperature and pressure were regulated. The contact radius, liquid volume and contact angle during evaporating were observed to study the influence of the gravity effect on the drop shape. The instant evaporation rate was calculated and compared with the theoretical prediction to analyse the coupling influence of diffusion and thermal convection. The effect of substrate temperature on the heat flux were also focused on. It was found that the evolution of heat flux density could be separated into four stages, began with the rapid increase and warm up, then switched to the long-time stable stage and ended up with the final rapid decrease stage.
A Firebug Optimal Cluster based Data Aggregation for Healthcare Application
Authors: Sree Ranjani N Y , Ananth A.G , Sudershan Reddy L 
Published: September 2022

Abstract
Majority of Wireless Sensor Networks (WSNs) are made up of huge sensor nodes with minimum cost, processing, communication capability and limited sensing. Because sensor nodes have limited resources, it’s critical to enhance bandwidth utilization, network lifetime and minimize the data transmission. The term data aggregation defines the method of integrating the sensor data to minimize the data transmission. However, WSN-based healthcare applications face privacy, and security concerns. In this paper design of a Fire Bug Swarm Optimal Cluster-Based Data Aggregation (FSOCDA) in WSN has been presented which is a useful method for WSN to save energy and extend network lifetime. The efficient clustering is carried out using FSO, which is utilized to choose the CH from among various nodes in the cluster that serve as data aggregation nodes. By altering the network scale, the computational competence of the FSOCDA technique is compared to that of the non-aggregate approach. Further, the data aggregation method is helpful in healthcare applications in terms of data transferring from host hospital to other.
Waste management in municipal markets
Authors: Merchán Diana , García-Cubero María Teresa , Paredes Alberto , González María 
Published: September 2022

Abstract
Municipal markets are approximately 90 years old in Ecuador, although some of them have modern buildings, the process of organic solid waste has not changed. Object of study of this work is the municipal market “Las Manuelas” located in the province of Guayas in the canton of Durán. Organic solid wastes could be used for the elaboration of new biopolymers, so the objective of this research is to determine the environmental impact of the activities articulated to this process. The observation technique was applied to make the process flow diagram and the methodology of the matrix of importance of environmental impacts. Results obtained showed that the activity of preparation of vegetables and fruits within the municipal market presented the highest I with a value of -42, under these parameters the need to establish preventive actions that can contribute to the circular economy was manifested.
Biodegradation of Plastic Using Termites and their Gut Microbiota: A Mini Review
Authors: Kumar Amit , Kalleshwaraswamy C. M. , Sharma Radhika , Sharma Parvati , Poonia Asha 
Published: September 2022

Abstract
Nature’s pollution is human created activity and human crosses the entire barrier to causing pollution and posing a high significant risk to health of all animals and also for plants. Plastics are the one of the major reason behind this. The high use of plastics material create high solid waste and this solid waste having no completely and fast degradation methods are available in nature. Their degradation also releases various harmful toxic substances which are harmful for environment. Various methods are available for treatment of plastic such as photo-oxidative degradation, thermal degradation, ozone-induced degradation, mechano-chemical degradation, catalytic degradation, and bio degradation. All these methods pose threat to the environment except the method of Biodegradation. Degradation of plastics using microbes are very eco-friendly degradation and causing no any side effect on nature. Termites are the major soil insect that are also capable to degrade plastics using their gut microbiota. Lot of microbial diversity present in termites gut but only few having potential to degrade plastics. In this review we mainly focus on the diversity of termites and their gut microbial fauna that having potential to degrade plastics and their different polymers.
Improving running-in quality and efficiency of journal bearing with ZB and SiO2 nanoparticles as lubricant additive
Authors: Zhou Yuankai , Wang Zhiyong , Wang Jiabiao , Zuo Xue , Xu Jing 
Published: September 2022

Abstract
To both improve the running-in quality and efficiency of the journal bearing, the Zinc borate and SiO2 combinations (ZB/SiO2) are used as lubricant additive. Running-in tests were carried out using a ring-on-block tribometer. Multifractal parameters and correlation dimensions were used to extract the nonlinear characteristics of surface topography and coefficient of friction (COF), respectively. It is also used to evaluate the running-in quality of journal bearing. The time of correlation dimension to reach the stable stage is used to evaluate the running-in efficiency. With the addition of ZB/SiO2, the correlation dimension of COF increases and the multifractal parameters of wear surfaces decrease significantly. Compared with the single ZB or SiO2 additive, the running-in efficiency of the ZB/SiO2 additive can be improved by 25% and 40%, respectively. It indicates that the running-in quality and efficiency are improved with the additive of ZB/SiO2. The excellent tribological properties of ZB/SiO2 are attributed to the solid lubricating film formed by ZB, as well as the rolling, self-repairing, and polishing effect of SiO2. The results are of great significance to improving the service reliability and extending the service life of the journal bearing.
Optimization of Wire-cut EDM process parameters using TLBO algorithm
Authors: Arya Rashmi , Singh Hari 
Published: September 2022

Abstract
The novelty of the work is reflected in using Teaching Learning Based Optimization as metaheuristic algorithm in combination with Response Surface Methodology for achieving multi response optimization of two conflicting responses while machining Incoloy 800H on Wire cut EDM. It is used in materials and equipment for furnace, petrochemical furnace cracker tubes, pigtails and headers, and electric heating element sheathing. There are a number of process parameters of Wire cut EDM which affect the output responses. Here, the effects of Pulse On Time, Pulse Off Time, Peak Current and Servo Voltage on the Cutting Rate and Dimensional Deviation were studied. Experiments were performed according to Box Behnken design with 29 experiemental runs. Pulse On Time was found to be the most significant factor in affecting the Cutting Rate and Dimensional Deviation as envisaged by Analysis of Variance . First, single response optimization was performed using Teaching Learning Based Optimization Algorithm which resulted in an optimal factor level setting of Pulse On Time-120mu, Pulse Off Time-45 mu, Peak Current-200A, Servo Voltage-47V corresponding to Cutting Rate, whereas, Pulse On Time-120mu, Pulse Off Time-35 mu, Peak Current-160 A, and Servo Voltage-30V formed an optimal setting of the factors corresponding to the Dimensional Deviation. Multi-response optimization resulted in an optimal factor level setting of Pulse On Time-120 mu, Pulse Off Time-38 mu, Peak Current-190 A, and Servo Voltage-53 V corresponding to both Cutting Rate and Dimensional Deviation. Then, the optimal factor level settings for single response and multi-response optimizations were confirmed through three confirmation tests. The average of the confirmation tests was compared with predicted value and percentage error was calculated. The percentage error comes out to be less than 5% in all the cases.
Hyper-diffusion on multiplex networks
Authors: Ghorbanchian Reza , Latora Vito , Bianconi Ginestra 
Published: September 2022

AbstractMultiplex networks describe systems whose interactions can be of different nature, and are fundamental to understand complexity of networks beyond the framework of simple graphs. Recently it has been pointed out that restricting the attention to pairwise interactions is also a limitation, as the vast majority of complex systems include higher-order interactions that strongly affect their dynamics. Here, we propose hyper-diffusion on multiplex networks, a dynamical process in which diffusion on each single layer is coupled with the diffusion in other layers thanks to the presence of higher-order interactions occurring when there exists link overlap. We show that hyper-diffusion on a duplex network (a multiplex network with two layers) can be described by the hyper-Laplacian in which the strength of four-body interactions among every set of four replica nodes connected in both layers can be tuned by a parameterδ11⩾ 0. The hyper-Laplacian reduces to the standard lower Laplacian, capturing pairwise interactions at the two layers, whenδ11= 0. By combining tools of spectral graph theory, applied topology and network science we provide a general understanding of hyper-diffusion on duplex networks whenδ11> 0, including theoretical bounds on the Fiedler and the largest eigenvalue of hyper-Laplacians and the asymptotic expansion of their spectrum forδ11≪ 1 andδ11≫ 1. Although hyper-diffusion on multiplex networks does not imply a direct ‘transfer of mass’ among the layers (i.e. the average state of replica nodes in each layer is a conserved quantity of the dynamics), we find that the dynamics of the two layers is coupled as the relaxation to the steady state becomes synchronous when higher-order interactions are taken into account and the Fiedler eigenvalue of the hyper-Laplacian is not localized in a single layer of the duplex network.
Estimating the Effectiveness of First Dose of COVID-19 Vaccine Against Mortality in England: A Quasi-Experimental Study
Authors: Bermingham Charlotte , Morgan Jasper , Ayoubkhani Daniel , Glickman Myer , Islam Nazrul , Sheikh Aziz , Sterne Jonathan , Walker A Sarah , Nafilyan Vahé 
Published: September 2022

Abstract
Estimating real-world vaccine effectiveness is vital to assessing the coronavirus disease 2019 (COVID-19) vaccination program and informing the ongoing policy response. However, estimating vaccine effectiveness using observational data is inherently challenging because of the nonrandomized design and potential for unmeasured confounding. We used a regression discontinuity design to estimate vaccine effectiveness against COVID-19 mortality in England using the fact that people aged 80 years or older were prioritized for the vaccine rollout. The prioritization led to a large discrepancy in vaccination rates among people aged 80–84 years compared with those aged 75–79 at the beginning of the vaccination campaign. We found a corresponding difference in COVID-19 mortality but not in non-COVID-19 mortality, suggesting that our approach appropriately addressed the issue of unmeasured confounding factors. Our results suggest that the first vaccine dose reduced the risk of COVID-19 death by 52.6% (95% confidence limits: 15.7, 73.4) in those aged 80 years, supporting existing evidence that a first dose of a COVID-19 vaccine had a strong protective effect against COVID-19 mortality in older adults. The regression discontinuity model’s estimate of vaccine effectiveness is only slightly lower than those of previously published studies using different methods, suggesting that these estimates are unlikely to be substantially affected by unmeasured confounding factors.
PGRNIG: novel parallel gene regulatory network identification algorithm based on GPU
Authors: Yang Bin , Bao Wenzheng , Chen Baitong 
Published: September 2022

AbstractMolecular biology has revealed that complex life phenomena can be treated as the result of many gene interactions. Investigating these interactions and understanding the intrinsic mechanisms of biological systems using gene expression data have attracted a lot of attention. As a typical gene regulatory network (GRN) inference method, the S-system has been utilized to deal with small-scale network identification. However, it is extremely difficult to optimize it to infer medium-to-large networks. This paper proposes a novel parallel swarm intelligent algorithm, PGRNIG, to optimize the parameters of the S-system. We employed the clone selection strategy to improve the whale optimization algorithm (CWOA). To enhance the time efficiency of CWOA optimization, we utilized a parallel CWOA (PCWOA) based on the compute unified device architecture (CUDA) platform. Decomposition strategy and L1 regularization were utilized to reduce the search space and complexity of GRN inference. We applied the PGRNIG algorithm on three synthetic datasets and two real time-series expression datasets of the species of Escherichia coli and Saccharomyces cerevisiae. Experimental results show that PGRNIG could infer the gene regulatory network more accurately than other state-of-the-art methods with a convincing computational speed-up. Our findings show that CWOA and PCWOA have faster convergence performances than WOA.
Towards assessing subcortical “deep brain” biomarkers of PTSD with functional near-infrared spectroscopy
Authors: Balters Stephanie , Schlichting Marc R , Foland-Ross Lara , Brigadoi Sabrina , Miller Jonas G , Kochenderfer Mykel J , Garrett Amy S , Reiss Allan L 
Published: September 2022

AbstractAssessment of brain function with functional near-infrared spectroscopy (fNIRS) is limited to the outer regions of the cortex. Previously, we demonstrated the feasibility of inferring activity in subcortical “deep brain” regions using cortical functional magnetic resonance imaging (fMRI) and fNIRS activity in healthy adults. Access to subcortical regions subserving emotion and arousal using affordable and portable fNIRS is likely to be transformative for clinical diagnostic and treatment planning. Here, we validate the feasibility of inferring activity in subcortical regions that are central to the pathophysiology of posttraumatic stress disorder (PTSD; i.e. amygdala and hippocampus) using cortical fMRI and simulated fNIRS activity in a sample of adolescents diagnosed with PTSD (N = 20, mean age = 15.3 ± 1.9 years) and age-matched healthy controls (N = 20, mean age = 14.5 ± 2.0 years) as they performed a facial expression task. We tested different prediction models, including linear regression, a multilayer perceptron neural network, and a k-nearest neighbors model. Inference of subcortical fMRI activity with cortical fMRI showed high prediction performance for the amygdala (r &gt; 0.91) and hippocampus (r &gt; 0.95) in both groups. Using fNIRS simulated data, relatively high prediction performance for deep brain regions was maintained in healthy controls (r &gt; 0.79), as well as in youths with PTSD (r &gt; 0.75). The linear regression and neural network models provided the best predictions.