Miracle-dz/UniFAR-FTUs huggingface dataset:
Centroid Localization Method in Wireless Sensor Networks Using TSK Fuzzy Modeling

Localization is one of the fundamental problems in wireless sensor networks (WSNs) that forms the basis for many location-aware applications. Localization in WSNs is to determine the position of node based on the known positions of several nodes. Most of previous localization method use triangulation or multilateration based on angle of arrival (AOA) or distance measurements. In this paper, we propose a enhanced centroid localization method using edge weights of adjacent nodes based on TSK fuzzy modeling. In the proposed method, first we find adjacent reference nodes which are connected to the node to be localized, and then develop the fuzzy membership function using genetic algorithms (GAs) for edge weights based on received signal strength indicator (RSSI) information. After calculating edge weights, we employ weighted centroid method to localize the node. Finally, we simulated the proposed method to demonstrate the performance.
A Band Stop Decoupling Structure for Circularly Polarized Antennas With Different Intervals

In this paper, a novel band stop decoupling structure (BSDS) for circularly polarized microstrip antenna array with different intervals to realize mutual coupling reduction is proposed. The BSDS is composed of complementary split-rings resonator (CSRRs) and split-rings resonator (SRRs), where the CSRRs block TM waves by electric resonance and the SRRs block TE waves by magnetic resonance, respectively. The proposed design is verified by the simulation and the results indicate that the isolation enhancement more than 35 dB can be obtained at the center frequency of 3.4 GHz when the center spacing is 0.37 $\lambda$0. Futhermore, when the center spacing reduces to 0.26 $\lambda$0, the mutual coupling remains below 25 dB. Besides, the BSDS can improve the radiation pattern offset without affecting the gain and the backward radiation of antennas.
Availability evaluation of flow networks with varying throughput-demand and deferred repair

The author presents some simple formulas for availability of flow networks with several levels of performance and variable throughput demands. The system comprises highly available independent components. Two main types of component failure are considered: (1) those where the corresponding repair can be deferred to a more convenient time, and (2) all other failures. This more convenient time is set to a fixed date in the present model. This model is relevant to gas/oil production and transportation systems. Such availability concepts as throughput, demand, and onstream are discussed; the throughput availability is usually the most important. The computation of these measures can be time consuming; therefore, efficient algorithms and formulas are vital. The calculations are fast, so that sensitivity of various factors can be studied easily. The calculations show which components are contributing the most to unavailability. >
The Problem of Power Spectrum and the Signal-To-Noise Ratio and Its Fast Algorithm Implementation in Gene Identification

This paper studies the gene identification model, which adopted the signal processing and analysis method to find the problem of power spectrum and the signal-to-noise ratio and its fast algorithm implementation of the gene coding sequence. Since Discrete Fourier transform algorithm has large computation, according to the principle that the far distance between the bases lead to a small interrelation in the power spectrum calculation and by descending to reduce the time complexity of the algorithm. We use 3-cyclical interception DNA fragment to structure a N/3 point to realize fast calculation of DNA sequence of power spectrum. The computer simulation results showed that the improved algorithm not only enhances the recognition effect of the first exon, but also reduces the time complexity of the original algorithm greatly.
Minimal Length Elements in Twisted Conjugacy Classes of Finite Coxeter Groups

Let W be a finite Coxeter group and let F be an automorphism of W that leaves the set of generators of W invariant. We establish certain properties of elements of minimal length in the F-conjugacy classes of W that allow us to define character tables for the corresponding twisted Iwahori–Hecke algebras. These results are extensions of results obtained by Geck and Pfeiffer in the case where F is trivial.
Steel Surface Defect Detection Based on Improved MASK RCNN

The defect detection of steel is an important process to ensure the quality of steel. The traditional detection methods have low efficiency and poor accuracy. With the development of deep learning and computer vision technologies, this paper proposes an improved Mask RCNN model for steel defect detection. The feature extraction network of Mask RCNN is replaced by a more robust EfficientNet, the improved BiFPN structure is combined with EfficientNet to extract features of different scales, and a CBAM module is added to the mask branch to improve the quality of mask prediction. Experiments on the Severstal steel surface defect dataset show that the improved method not only significantly improves the accuracy of the model, but also greatly reduces the model parameters.
Recent Advancement in Autonomous Vehicle and Driver Assistance Systems

In past two decades, the popularity and use of autonomous vehicles and driver assistance systems has accelerated due to the advancement in artificial intelligence techniques, big data analysis, machine learning, computer vision, smart sensors and Internet of Things (IoT). Autonomous vehicles (AVs) are gaining popularity because it reduces traffic congestion along with increase in safety and fuel efficiency. The present paper focuses on the functionality of the driver-less car and Advanced Driver & Driving Assistance Systems (ADDAS). Driver assistance systems help drivers to driving safely by avoiding and minimising distractions by observing their actions. Artificial Intelligence can be incorporated when AVs drive alongside human-driven vehicles (HVs), also known as the era of Mixed Autonomy. The Markov decision process (MDP) can be used to represent the communication between the driver-less vehicles and the surroundings, where, MDP is a discrete-time imaginary command process. MDP is utilized in various fields like automation, economics, manufacturing, and automatic control. This paper provides a detailed review on recent research and developments on autonomous vehicles and ADDAS. Some onfield examples of ADDAS are Automatic Parking, Automatic Braking System (ABS), Blind Spot Monitors, Adaptive Cruise Control (ACC), etc.
Long term outcomes of occipital nerve stimulation

Background Occipital nerve stimulation (ONS) has been investigated as a potential treatment for disabling headaches and has shown promise for disorders such as chronic migraine and cluster headache. Long term outcomes stratified by headache subtype have had limited exploration, and literature on outcomes of this neuromodulatory intervention spanning 2 or more years is scarce. Measures We performed a narrative review on long term outcomes with ONS for treatment of headache disorders. We surveyed the available literature for studies that have outcomes for 24 months or greater to see if there is a habituation in response over time. Review of the literature revealed evidence in treatment of occipital neuralgia, chronic migraine, cluster headache, cervicogenic headache, short lasting unilateral neuralgiform headache attacks (SUNHA) and paroxysmal hemicrania. While the term “response” varied per individual study, a total of 17 studies showed outcomes in ONS with long term sustained responses (as defined per this review) in the majority of patients with specific headache types 177/311 (56%). Only 7 studies in total (3 cluster, 1 occipital neuralgia, 1 cervicogenic headache, 1 SUNHA, 1 paroxysmal hemicrania) provided both short-term and long-term responses up to 24 months to ONS. In cluster headache, the majority of patients (64%) were long term responders (as defined per this review) and only a minority of patients 12/62 (19%) had loss of efficacy (e.g., habituation). There was a high number 313/439 (71%) of adverse events per total number of patients in the studies including lead migration, requirements of revision surgery, allergy to surgical materials, infection and intolerable paresthesias. Conclusions With the evidence available, the response to ONS was sustained in the majority of patients with cluster headache with low rates of loss of efficacy in this patient population. There was a high percent of adverse events per number of patients in long term follow-up and likely related to the off-label use of leads typically used for spinal cord stimulation. Further longitudinal assessments of outcomes in occipital nerve stimulation with devices labelled for use in peripheral nerve stimulation are needed to evaluate the extent of habituation to treatment in headache.
The Influence of Accounting Information Systems and Service Systems on Organizational Performance at Hospital in Makassar City

This study aims to analyze the accounting information system in hospitals. This study uses primary data sources by distributing questionnaires to 63 respondents with research samples, namely the head of service, head of support, and head of financial administration at type A and type B hospitals in Makassar City. The returned questionnaire was 57 respondents, processed using multiple linear analysis with the help of the SmartPLS computer program. The results showed that the accounting information system and service system have a role in improving organizational performance at hospitals in Makassar City.
Validation and discussion of clinical practicability of the 2022 graded prognostic assessment for NSCLC adenocarcinoma patients with brain metastases in a routine clinical cohort

Introduction The goal of this analysis is to validate the 2022 graded prognostic assessment (GPA) for patients with brain metastases from adenocarcinoma of the lung and to discuss its clinical practicability. Methods/material 137 patients with adenocarcinoma of the lung were included in this analysis. The disease specific GPA for NSCLC, Lung-molGPA and the GPA for NSCLC adenocarcinoma were calculated. Overall survival was calculated for each GPA group. Additionally, expected and actual OS in the prognostic groups of the GPA available at the time of the patients’ diagnosis was compared. Results Median overall survival (OS) from diagnosis of brain metastases was 15 months (95% confidence interval (CI) 9.7–20.3 months). The median OS in the three individual prognostic groups was 7 months for GPA 0-1, 16 months for GPA 1.5-2, 33 months for GPA 2.5-3 and not reached for GPA 3.5-4 (p<0.001). Median survival times for the individual groups were similar to those published in the original GPA publication. Regarding the expected and actual OS when using the available GPA at the time of diagnosis there was an underestimation of survival of more than 3 months for all except the worst prognosis group. Conclusion We were able to validate the 2022 GPA for NSCLC adenocarcinoma patients with brain metastases in a similar cohort from a non-academic center. However, the practical applicability regarding the expected median OS might be limited due to the constantly evolving treatment landscape and the consecutive improvement in overall survival.
A Novel Link Prediction Method for Multiplex Networks with Incomplete Information

The study of network disruption has attracted much attention for its wide range of applications, including controlling the spread of epidemics, disrupting criminal networks, and controlling the spread of rumors, where the key is to find the key nodes in the network. Because the information is incomplete, the results of identifying key nodes are not accurate. In order to reduce the influence of incomplete information on network analysis, a link prediction algorithm based on multiplex networks' s characteristics is proposed. Multiplex networks will be divided into two parts: target layer and other layer, in which the target layer is incomplete information network. Each of other layers is assigned a weight, indicating how similar the network is to the target layer, while the edges of other layers were assigned a value, indicating their importance. The score of an edge is multiplied with the weight of the layer it is in, and the product of edges with the same endpoint is summed, and the result is used as the final score of edges with the same endpoint. Target layer will be supplemented by the selection of high-scoring edges. A multiplex network named Aarhus Computer Science Department was used. As a target layer, Facebook falls into three categories: complete information, incomplete information, and supplementary information. In each of these three cases, network collapse was performed. Experimental results show that this algorithm has higher AUC value and Precision than three classical link prediction algorithms based on common neighborhoods, preferential attachment and Jaccard index. In the case of complete information and supplementary information, the order of node deletion in network collapse is basically the same, which shows that this algorithm can effectively reduce the impact of incomplete information on network collapse.
Mode of Antifungal Action of Daito-Gettou (Alpinia zerumbet var. exelsa) Essential Oil against Aspergillus brasiliensis

Plant-derived essential oils (EOs) are used in medicines, disinfectants, and aromatherapy products. Information on the antifungal activity of EO of Alpinia zerumbet var. exelsa (known as Daito-gettou) found in Kitadaito Island, Okinawa, is limited. Therefore, we aimed to evaluate the antifungal activity of EOs obtained via steam distillation of leaves of Daito-gettou, which is a hybrid of A. zerumbet and A. uraiensis. Daito-gettou EO showed antifungal activity (minimum inhibitory concentration = 0.4%) against Aspergillus brasiliensis NBRC 9455, which was comparable to that of A. zerumbet found in the Okinawa main island. Gas chromatography/mass spectrometry revealed that the main components of Daito-gettou EOs are γ-terpinene, terpinen-4-ol, 1,8-cineole, 3-carene, and p-cymene. Terpinen-4-ol content (MIC = 0.075%) was 17.24%, suggesting that the antifungal activity of Daito-gettou EO was mainly attributable to this component. Daito-gettou EO and terpinen-4-ol inhibited mycelial growth. Moreover, calorimetric observations of fungal growth in the presence of Daito-gettou EO showed a characteristic pattern with no change in the initial growth rate and only a delay in growth. As this pattern is similar to that of amphotericin B, it implies that the action mode of Daito-gettou EO and terpinen-4-ol may be fungicidal. Further studies on the molecular mechanisms of action are needed for validation.
A Literature Review of Bitcoin Network Infrastructure, Methodology, and Challenges*

Cryptocurrency has become a significant business stream in recent years for investors worldwide. Through a giant decentralized bitcoin network, cryptocurrency solutions have been extended to world investors over the internet. Understanding the underlying infrastructure of the bitcoin network is essential for internet users to realize the business environment for their investment and for researchers to improve the provisioning of cryptocurrency solutions. This paper provides a literature review to illustrate the primary methodology of bitcoin networks, underlying infrastructure, and the significant technical challenges and security issues. We describe the blockchain network and its components and structure. We also discuss the bitcoin transaction handling over the blockchain networks and the cryptographic techniques for securing the blockchain networks and cryptocurrency solutions.
New Multi-Objective Genetic Algorithms for Diversity and Convergence Enhancement

We have developed new multi-objective evolutionary algorithms to improve convergence and diversity of Pareto front surface for multi-objective optimization. With Fonseca and Fleming’s Multi-Objective Genetic Algorithms (MOGA) as a baseline algorithm, we propose an elite-preserving MOGA (EP-MOGA). In addition, we suggest a new sharing function coupled with an index-based priority for better diversity and convergence properties than the standard sharing function approach. In order to further enhance convergence to the Pareto front, a novel directional operator is developed, comprising selection of search direction and local one-dimensional search. The directional method drives design solutions normal to the plane passing extreme solutions as the NBI (Normal Boundary Interaction) method does. The proposed algorithms are named EP-MOGAS (Elite Preserving MOGA with a new Sharing function) and EP-MOGASD with the directional operator added. The new methods are applied to several standard test functions and a multi-objective compressor rotor design problem. In the test problems, comparisons are made for EP-MOGA, EP-MOGAS, EP-MOGAS-D and NSGA-II. Results show the new sharing function remarkably improves convergence and diversity of Pareto solutions. The new directional operator is found to enhance the convergence greatly while maintaining the diversity of solutions except for ZDT4, a test function with many local optimal Pareto fronts. Finally, a multi-objective shape design of compressor rotor blades confirms applicability and effectiveness of the present algorithms to real world constrained multi-disciplinary design optimization problems.
A Wearable RF Energy Harvester Based on Spoof Surface Plasmonic Antenna Array

This paper presents a wearable RF energy harvester based on spoof surface plasmonic (SSP) antenna array. Four separate antennas are connected by the four bending SSP waveguides, and the SSP antenna array is formed. The loop is applied to excite the SSP structure to generate surface waves and converted to the radiative wave into the space. The rectify circuit is integrated with loop to output DC power and fabricated by the polyimide so that it can conform with different sensing modules. The proposed harvester is fabricated by the conductive and nylon fabrics in a compact and low-profile structure to be easily integrated with clothing. The maximum harvested RF-DC power is 126.24 $\mu W$ under a power density of $2.75 \mu W/cm^{2}$ when the proposed structure is worn on phantom. The simulation and measurement results demonstrate that the wearable RF energy harvester has the potential to power body sensor network.
On molecular graphs and digraphs of annulenes and their spectra

(Received 25 December 2000)Amolecular graph, consisting of undirected edges, can be represented as a sumof two digraphs, consisting of oppositely oriented directed edges. In the case ofannulenes, the eigenvalue spectrum of the molecular graph is equal to the sum of theeigenvalue spectra of the respective two molecular digraphs.
Reduced‐order modeling of parameterized PDEs using time–space‐parameter principal component analysis

This paper presents a methodology for constructing low‐order surrogate models of finite element/finite volume discrete solutions of parameterized steady‐state partial differential equations. The construction of proper orthogonal decomposition modes in both physical space and parameter space allows us to represent high‐dimensional discrete solutions using only a few coefficients. An incremental greedy approach is developed for efficiently tackling problems with high‐dimensional parameter spaces. For numerical experiments and validation, several non‐linear steady‐state convection–diffusion–reaction problems are considered: first in one spatial dimension with two parameters, and then in two spatial dimensions with two and five parameters. In the two‐dimensional spatial case with two parameters, it is shown that a 7 × 7 coefficient matrix is sufficient to accurately reproduce the expected solution, while in the five parameters problem, a 13 × 6 coefficient matrix is shown to reproduce the solution with sufficient accuracy. The proposed methodology is expected to find applications to parameter variation studies, uncertainty analysis, inverse problems and optimal design. Copyright © 2009 John Wiley & Sons, Ltd.
A study of irregular singular algebraic connections on curves: Monodromies, canonical decompositions and an example of the determinant of period integrals

Abstract : In this thesis, we study three aspects of irregular connections on curves: monodromies, canonical decompositions and the period determi-nant. In chapter 1, we recall a theorem due to Deligne that any rank 1 represen-tation of the fundamental group of an affine curve can appear as a connection on the trivial bundle. Then we make its generalization into two directions: firstly, we show the obstruction of the problem for rank 1 representations is the torsion of H 2 (U, Z ) for an affine variety U of any dimension. Secondly, over an affine curve the theorem holds for a representation of any rank. In chapter 2, from the view of Beilinson, Bloch and Esnault, we show the classical decomposition theorems of formal connections in one variable known as the slope decomposition and the Turritin-Levelt decomposition are absolute if the connection is integrable. Finally, in chapter 3, we calculate the determinant of the period integrals for an irregular connection r = d + dy over a Legendre elliptic curve E : y2 = x(x - 1)(x - lambda).
Extraction of outline feature points based on the minimum approach error

An algorithm was developed to extract outline feature points and to approximate polygon using a polygon approximation error and a local minimum approach error for the feature points. The feature points divide the outline into a tree form with the minimum approach error, using the least number of feature points for a given approach error and minimum approach error. The feature points are used to feature extraction, optimization of polygon approximations, and compression of the outline curve.
Estimation of the total cloud cover with high temporal resolution and parametrization of short-term fluctuations of sea surface insolation

Sky observations by means of digital full sky imagers enable a continuous monitoring and archiving of the state of the atmosphere. A direct application is the estimation of the total amount of clouds with a very high temporal resolution. From simple methods of image analysis, we used the full sky imager additionally as a sunshine recorder to replace complex pyrheliometric and pyranometric measurements for detecting direct solar irradiation. The Meridional Ocean Radiation Experiment MORE has realized four transatlantic research cruises with a special focus on the surface radiation budget, remote sensing of clouds and automated sky monitoring. This dataset was used to study different parametrizations of solar downward radiation at the sea surface based on standard meteorological measurements. It was found that the parameterization by ZILLMAN (1972) provided the best performance for the use with operational meteorological data. Furthermore, this parametrization was modified to create a fast responding parametrization to reproduce short-term fluctuations of the insolation. This new parametrization has been applied to independent datasets. It is shown that based on measurements of a few weeks it was not possible to reduce the overall bias of parameterized insolation significantly due to the fact that the parametrization does not consider the optical thickness of clouds. The standard deviation between calculated and measured flux has been reduced by 4%. The performance for separated broken cloud conditions was improved significantly.