Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-Series Model for Climatological Forest Fire Prediction over Borneo

Areas covered by tropical forests, such as Borneo, are vulnerable to fires. Previous studies have shown that climate data is one of the critical factors affecting forest fire. This study aims to predict the forest fire over Borneo by considering the temporal aspects of the climate data. A time seriesbased model, Long Short-Term Memory (LSTM), is used. Three LSTM models are applied: Basic LSTM, Bidirectional LSTM, and Stacked LSTM. Three different experiments from January 1998 to December 2015 are conducted by examining climate data, Oceanic Nino Index (ONI), and Indian Ocean Dipole (IOD) index. The proposed model is evaluated by Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and correlation number. As a result, all models can capture the spatial and temporal pattern of the forest fires for all three experiments, in which the best prediction occurs in September with a spatial correlation of more than 0.75. Based on the evaluation metrics, Stacked LSTM in Experiment 1 is slightly superior, with the highest annual pattern correlation (0.89) and lowest error (MAE= 0.71 and RMSE=1.32). This finding reveals that an additional ONI and IOD index as the prediction features would not improve the model performance generally, but it specifically improves the extreme event value.
Linear Operators and their Spectra

Preface 1. Elementary operator theory 2. Function spaces 3. Fourier transforms and bases 4. Intermediate operator theory 5. Operators on Hilbert space 6. One-parameter semigroups 7. Special classes of semigroup 8. Resolvents and generators 9. Quantitative bounds on operators 10. Quantitative bounds on semigroups 11. Perturbation theory 12. Markov chains and graphs 13. Positive semigroups 14. NSA Schrodinger operators.
Proceedings of HLT/EMNLP on Interactive Demonstrations

This volume contains the abstracts of the technology demonstrations that were presented at the combined 2005 Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, held in Vancouver, British Columbia, Canada on October 7, 2005. The demonstration program has traditionally been an important component of the HLT conference, and the addition of EMNLP this year creates a new opportunity for interaction among researchers coming from a variety of methodological perspectives. The demonstration program allows researchers the opportunity to showcase new and innovative technology, to update the research community on ongoing projects, and to show software tools developed for corpus development or other research aids to the users they were designed to assist. Demo technologies included in the program were selected based on their potential appeal to the HLT/EMNLP audience, technical and innovative merit, and completeness as a stand-alone demo. Out of 31 demonstration proposals submitted for review, 20 were selected for inclusion at the conference, representing 32 institutions from Asia, North America, and Europe.
Arbitration Mechanisms for Multiple Entry Capability in PBFT for IoT Systems

Practical Byzantine Fault Tolerance (PBFT) is a widely used consensus protocol which is sensitive to malicious behavior of the designated leader. In this paper we discuss two mechanisms that allow any ordering node on the consensus committee to act as the leader, thus alleviating the dependency on the leader. The selection of the next leader is performed by arbitration, rather than through a predefined sequence or round-robin mechanism. As the result, the proposed mechanisms lead to improved security since a malicious leader cannot stall the consensus and the next leader is not known beforehand. Performance evaluation shows that the proposed mechanisms indeed offer independence of the chosen leader and a reduction of queuing times of client proposals, albeit with some performance degradation.
Hybrid Deep Autoencoder with Random Forest in Native SDN Intrusion Detection Environment

This paper introduces a hybrid deep autoencoder with a random forest classifier model to enhance intrusion detection performance in a native SDN environment. A deep learning architecture combining a deep autoencoder with random forest learning feature representation of traffic flows natively collected from the SDN environment. Publicly available packet Capture (PCAP) files of recorded traffic flows were used in the SDN network for flow feature extraction and real-time implementation. The results show very high and consistent performance metrics, with an average of 0.9 receiver-operating characteristics area under curve (ROC AUC) recorded. Furthermore, we compared the performance achieved using the original dataset with previous research to investigate the performance achieved using the same model developed. The flow-based intrusion model presented outperforms other publicly available methods, with a traffic anomaly detection rate of 98% accuracy and precision.
Interpersonal Adaptation, Self-Efficacy, and Metacognitive Skills in Italian Adolescents with Specific Learning Disorders: A Cross-Sectional Study

This study aimed to explore interpersonal adaptation, generalized self-efficacy, and metacognitive skills in a sample of Italian adolescents with and without a specific learning disorder (SLD). A total of 564 secondary and high school students (males = 236; females = 328; age range: 11–19; M = 16.14, SD = 1.70) completed a set of standardized tests assessing social and interpersonal skills (non-affirmation, impulsiveness, narcissism, social preoccupation, and stress in social situations), general self-efficacy, and metacognition. Students with SLD reported a lower interpersonal adaptation than students without SLD. Furthermore, students with SLD were more impulsive and had more problems handling social situations. They also reported lower levels of self-efficacy but higher metacognition scores than peers without SLD. The use of compensatory tools was associated with better interpersonal skills and higher levels of self-efficacy in students with SLD. Finally, using these instruments is predictive of high levels of metacognitive skills in adolescents with SLD. In line with the previous literature, this study showed the presence of a gap between adolescents with and without an SLD in terms of interpersonal adaptation, general self-efficacy, and metacognitive skills in the school context. Further studies are needed on the psychological well-being of adolescents with SLD and especially on the protective role of personal, social, and environmental characteristics.
Synthesis and Photophysics Characterization of Boronic Styril and Distyryl BODIPYs for Water-Based Dye-Sensitized Solar Cells

In this study, two boronic acid BODIPYs are obtained through a microwave-assisted Knoevenagel reaction. The aim is to use them for the first time as dyes in a photosensitized solar cell (DSSC) to mimic chlorophyll photosynthesis, harvesting solar light and converting it into electricity. The microwave-assisted Knoevenagel reaction is a straightforward approach to extending the molecular conjugation of the dye and is applied for the first time to synthesize BODIPY’s boronic acid derivatives. These derivatives have proved to be very useful for covalent deposition on titania. This work studies the photo-physical and electrochemical properties. Moreover, the photovoltaic performances of these two new dyes as sensitizers for DSSC are discussed. Experimental data show that both dyes exhibit photosensitizing activities in acetonitrile and water. In particular, in all the experiments, distyryl BODIPY was more efficient than styryl BODIPY. In this study, demonstrating the use of a natural component as a water-based electrolyte for boronic BODIPY sensitizers, we open new possibilities for the development of water-based solar cells.
Use of Ensemble Based Approach to Predict Health Insurance Premium at Early Stage

Health insurance is one of the most critical purchases a person makes every year. One-third of GDP is spent on health insurance, and everyone requires some degree of health care. The healthcare rates constantly fluctuating every year due of different variables such as medical changes, pharmaceutical trends, and political considerations. There is a need to construct a mathematical model to anticipate premiums based on numerous criteria that effect the rates. The premium rates are established by insurance companies using complicated algorithms based on past years' health care consumption and the total number of enrollments. In this research, an ensemble-based regression model to forecast future premiums is suggested. The suggested model is compared with the usual four regression models. It is demonstrated using multiple metrics that the suggested model always produces superior outcomes.
Over-the-Air Performance Testing of 5G New Radio User Equipment: Standardization and Challenges

The Third Generation Partnership Project (3GPP) is accelerating 5G new radio (NR) global standards aimed at significant enhancement of wireless system performance for higher date rate, better energy efficiency, and higher reliability than the current 4G cellular systems. The operators, manufacturers, and test equipment vendors have worked together to develop standardized over-the-air (OTA) test methodologies for the overall performance evaluation of 5G NR devices. 3GPP is taking the lead in standardizing the OTA testing of 5G NR under fading channel conditions. In 3GPP specifications, test methods have been studied to verify the multiple-input multiple-output (MIMO) performance of 5G NR user equipments (UEs) in OTA mode. This article follows the 3GPP standardization work and discusses the MIMO OTA test methodologies for 5G NR UEs working at frequency range 1 (FR1) and FR2, with a focus on its new challenges and solutions compared to 4G MIMO OTA testing methods. Then the OTA throughput testing results of real 5G NRUEs are demonstrated under the standard channel models. Finally, the challenges and limitations of standard 5G MIMO OTA test solutions are also highlighted.
Voltage-Dip Analysis of Brushless Doubly Fed Induction Generator Using Reduced T-Model

This paper presents the performance analysis of brushless doubly fed induction generator (BDFIG) during symmetrical voltage dips. The equivalent circuit consists of resistances and dependent voltage sources in its rotor loop; thus, its voltage-dip analysis becomes more challenging. To overcome such difficulty, a reduced full-order model of the BDFIG into a new T-model is presented. A detailed mathematical analysis is performed subject to voltage-dip conditions. The time variation for the machines fluxes, electromotive forces, voltages, currents, and active and reactive powers are analyzed and their analytical approaches are derived. The current/voltage stress of power converter during voltage dip is discussed. The accuracy of the proposed T-model and the theoretical voltage dip is confirmed via experimental tests on a 3-kW BDFIG, and simulation results of a 2-MW BDFIG.
Predictive Modelling of Axisymmetric Toroidal Configurations

The Corsica 1-1/2D transport code [1], now a part of the LLNL/GA Caltrans transport code, has been used to simulate a variety of toroidal configurations both to predict expected behavior and to analyze existing experimental data. The principal purpose of this paper is to show Corsica’s capability rather than in depth analyses of specific configurations. Much of this work emphasizes the influence of Ohm’s law although we do include studies with temperature transport and driven current and heat sources. A special feature of this code is its ability to treat tokamaks in addition to both spheromaks and RFP’s. The latter requires solving transport equations in poloidal flux coordinates rather than the standard toroidal flux coordinates.
Organic Complementary Inverter Circuits Fabricated with Reverse Offset Printing

p‐Type and n‐type organic thin film transistors (OTFTs) and complementary inverter circuits with finely patterned electrodes are fabricated by reverse offset printing. The electrodes achieve a channel length of less than 3 µm under optimized printing conditions. High‐performance OTFTs are fabricated using these electrodes and printed p‐type and n‐type organic semiconductors, each achieving a mobility of 0.2 cm2 V−1 s−1 at a channel length of 50 µm. A complementary inverter circuit fabricated with a stacked OTFT structure is demonstrated using reverse offset printing. The inverter circuits successfully operate at a supply voltage as low as 2.5 V with a high signal gain of 14. These results are expected to contribute greatly to the development of integrated circuits with high‐speed operation using OTFTs.
A novel mobile multi-user LiFi system

In this paper, a vehicular light-fidelity (LiFi) system, which supports mobility for seamless broadband data transmission is proposed for multipoint-to-point (M2P) communication. To this end, a novel digital mirror device (DMD) based receiver (RX) is investigated that consists of two functionalities, to sense the transmitters and sense mobility. Relying on the proposed hybrid waveform, the proposed RX has the ability to detect and distinguish multiple transmitter (TX) sources under free movement conditions within three-dimensional space. Computer simulation results for the ‘TX sensing platform’ show that a high TX detection and identification performance is achieved at an extremely low channel signal-to-noise-ratio (SNR) of −11 dB and a bit error ratio (BER) of 10−4. Furthermore. the simulation results for the ‘mobility sensing platform’ was able to trace the positions of light sources simultaneously with the error rate of below 2% relative to the diameter of light spots on the DMD for all considered mobility scenarios.
Effects of Resveratrol Against Induced Metabolic Syndrome in Rats: Role of Oxidative Stress, Inflammation, and Insulin Resistance

Metabolic syndrome (MS) is a serious health problem associated with an increase in risk factors for hepatic steatosis, which is the most common liver disease today. The goal of this study was to investigate the protective effects of resveratrol against metabolic alterations associated with a high-fat high-fructose diet (HFFD). Thirty-two male rats were randomly divided into four equal groups: control (cont.), metabolic syndrome (MS), resveratrol (Res), and metabolic syndrome treated with resveratrol (MS + Res). Resveratrol was administrated orally at a dose of 30 mg/kg·bw, daily. After 10 weeks, body weight, serum biochemical parameters, hepatic oxidative stress, inflammatory markers, as well as mRNA levels of hepatic genes related to lipid metabolism and insulin signaling were measured. In addition, the liver was examined histopathologically to detect lipid deposition. Increased body weight, hepatic dysfunction, dyslipidemia, hepatic insulin resistance, hepatic oxidative and inflammatory stress conditions, upregulation of mRNA expression level of sterol regulatory element binding protein 1-c (SREBP1-c), and downregulation of mRNA expression levels of peroxisome proliferated activated receptor alpha (PPARα) and insulin receptor substrate-2 (IR-S2) were all observed in the MS rats. Hepatic steatosis was confirmed by hematoxylin and eosin and Oil Red O staining. Administration of resveratrol reduced liver steatosis, oxidative stress, and inflammatory state. Also, it improved lipid profile as well as insulin sensitivity and reverted alterations in hepatic mRNA expression levels of the tested genes. Based on these findings, resveratrol could be proposed as a therapeutic approach for MS prevention.
Trajectory Planning for Multiple UAVs in UAV-aided Wireless Relay Network

The integration of Unmanned Aerial Vehicle (UAV) as flying base station (FBS) assisted wireless communications has recently attracted significant attention. However, there is a need for efficient optimization trajectory techniques to allow for continuous service connectivity for FBSs operating at regions beyond single-hop backhauling to their depot (i.e., fixed macro base station). This work proposes an optimization trajectory planning framework to provide continuous service delivery of FBSs by utilizing aerial relay nodes which can also serve ground users (GUs). The proposed joint optimization framework of FBSs trajectories where a subset of them acts as relay nodes called relay-FBS (rFBS) provides tangible gains compared to previously proposed nominal techniques. More specifically, a wide set of numerical investigations reveals that the proposed framework reduces aggregate travel time (ATT) more than 14% for the entire fleet of FBSs, whilst for rFBS to enable continuous service support, this average gain is more than 38%.
Rate-Constrained Network Decomposition for Clustered Cell-Free Networking

Base-stations (BSs) will be densely deployed to provide seamless coverage in sixth generation (6G) mobile communication systems, which, unfortunately, leads to severe cell-edge problem. A flexible clustered cell-free networking scheme to replace the cellular network is studied in this paper, which decomposes the whole network into a number of subnetworks operating independently. In order to reduce signaling overhead and system complexity as far as possible, we aim to maximize the number of decomposed subnetworks with a per-user rate constraint for service quality guarantee. In addition, subnetworks with BSs only are allowed to enable BS sleep mode operation. A rate-constrained network decomposition (RC-NetDecomp) algorithm is proposed, which can smoothly tune the network structure from the current cellular network to the fully cooperative network by varying the required per-user rate. Simulation results demonstrate that it outperforms the existing baselines in terms of both average per-user rate and fairness among users.
Age of Processed Information Minimization over Fading Multiple Access Channels

In many real-time applications, timely accessibility to actionable insights, usually obtained by processing raw status updates, is an important requirement. In this paper, we consider users equipped with sensors and actuators. The sensors of a user generate status updates which need to be processed for driving the actuators of the user. The processing task of a user can be carried out locally or it can be offloaded to an edge server, located at a common base station (BS). The users access the BS via a fading multiple access channel using a non-orthogonal multiple access scheme. To measure the timeliness of processed information, we adopt a metric called age of processed information (AoPI), defined as the time elapsed since the generation of last successfully processed packet available at the user. In this setting, we formulate a problem for minimizing a long-term average AoPI across the users, when the users are subjected to average power constraints. The BS has to decide when a user should generate a status update and whether it should be processed locally or at the edge server. We cast the problem as a constrained Markov decision process (CMDP) and solve it via Lagrangian relaxation. We derive several structural properties of the optimal solution to the relaxed problem. We also obtain a simpler policy via Lyapunov optimization and bound its performance. Using numerical simulations, we demonstrate the derived structural properties and compare average AoPIs under different policies.
The Effect of Severe Contralateral Carotid Stenosis or Occlusion on Early Outcomes after Carotid Endarterectomy

BACKGROUND: Stenosis of the carotid arteries, as a consequence of atherosclerosis is the most common cause of cerebrovascular insult (CVI). Severe (>70%) contralateral stenosis or occlusion (SCSO) of the carotid artery may represent an additional pre-operative risk factor for neurologic incidents. AIM: The aim of this study was to confirm and compare early perioperative results (0-30 days) of carotid endarterectomy (CEA) in patients with and without SCSO. PATIENT AND METHODS: In our retrospective-prospective study, we analysed the results of 273 CEA, divided into two groups based on the presence of significant contralateral stenosis or occlusion (non-SCSO and SCSO groups) RESULTS: 273 CEA’s were performed, divided into two groups: SCSO groups 40 (14.7%) and non-SCSO group 233 (85.3%). Between the two groups, a statistically significant difference between patients was found (54.1% compared to 87.5%; p<0.0005), CEA with patch angioplasty (25.3% compared to 52.5%; p=0.001), and CEA with the use of a shunt (3.9% compared to 35%; p<0.0005) in favour of the SCSO group. There was no statistically significant difference (SCSO was not identified as a risk factor) for any type of stroke or mortality. Logistically regression confirmed SCSO to be an independent predictor of 30-day mortality (OR 21.58; 95% CI 1.27-36.3; p= 0.033) and any type of stroke or mortality (OR 9.27; 95% CI 1.61-53.22; p= 0.012). SCSO was not a predictor of any type of stroke within 30 days. Predictors of any type of stroke was dyslipidemia (OR 0.12, 95% CI 0.02-0.76; p= 0.024). CONCLUSIONS: There was no statistically significant difference in the incidence of early (30 day) perioperative complications between the analysed groups. The percentage of perioperative complications remains within the accepted parameters, and thus, SCSO should not be qualified as a significant risk factor for CEA. We are of the opinion that CEA remains a safe and acceptable options for patients with SCSO, and SCSO should not be a reason for preferential use of carotid stenting.
Millimeter-Wave Received Power Prediction from Time-Series Images Using Deep Learning

Deep learning is applied to predict received power in a 60 GHz band propagation model from time-series images. Both three-dimensional (3D) convolutional neural network (CNN) and the combination with long short-term memory (CNN+LSTM) are used to construct predictive models. The CNN and CNN+LSTM models are evaluated to predict the received power up to 2 seconds ahead with root-mean-square errors of 2.81 dB and 2.27 dB, respectively. In addition, Kirchhoff approximation (KA) is applied as an efficient data generator to calculate the received power analytically for the corresponding propagation model. The calculated received power values are compared with measured results to validate the formulation.
Process-parallel center deviation measurement of a BTA deep-hole drilling tool

Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected, information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of maximization. The study of capacity optimization and costing models is an important research topic that deserves contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization’s value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017.