Miracle-dz/UniFAR-FTUs huggingface dataset:
Election Forecasting with Machine Learning and Sentiment Analysis: Karnataka 2023

Data science is rapidly transforming the political sphere, enabling more informed and data- driven electoral processes. The ensemble machine model which is made up of Random Forest Classifier, Gradient Boosting Classifier, and Voting Classifier, introduced in this paper makes use of machine learning methods and sentiment analysis to correctly forecast the results of the Karnataka state elections in 2023. Election features such as winning party, runner- up party, district name, winning margin, and voting turnout are used to evaluate the effectiveness of different machine learning paradigms. Similarly, it also makes use of sentiment analysis through party tweet and public reactions for further breaking down reliance upon past elections data alone. This study demonstrates that using both past historical records and current public opinion yields precise predictions about how electable leaders are. This reduces reliance on a historical dataset. The experimented results shows that, how machine learning and sentiment analysis can predict election results and provide useful data for election decision making. We compared various machine learning models in this study, including logistic regression, Grid SearchCV, XGBoost, Gradient Boosting Classifier, and ensemble model. With an accuracy of 85%, we demonstrated that our ensemble model outperformed machine models such as XGBoost and Gradient Boosting Classifier. It also offers a novel method for predictive analysis.
Annual maps of forest cover in the Brazilian Amazon from analyses of PALSAR and MODIS images

Abstract. Many forest cover maps have been generated by using optical and/or microwave images, but these forest cover maps have large area and spatial discrepancies. To date, few studies have assessed forest cover maps in terms of two biophysical parameters used in forest definition: canopy height and canopy coverage. We generated annual forest cover maps from 2007 to 2010 and evergreen forest cover maps from 2000 to 2021 in the Brazilian Amazon using the images from the Phased Array type L-band Synthetic Aperture Radar and the time series images from the Moderate Resolution Imaging Spectroradiometer, using the forest definition of the Food and Agriculture Organization of the United Nations (>5 m tree height and >10 % canopy coverage) as the reference. We used the canopy height and canopy coverage datasets from the Geoscience Laser Altimeter System during 2003–2007 to assess annual forest cover maps from 2007 to 2010 and annual evergreen forest cover maps from 2003 to 2007, and the results show high accuracy of these forest cover and evergreen forest cover maps. These annual forest cover maps and annual evergreen forest cover maps provide data support for the analyses of the causes, processes, and consequences of forest cover changes in the Brazilian Amazon (https://doi.org/10.6084/m9.figshare.21445626; Qin and Xiao, 2022a; https://doi.org/10.6084/m9.figshare.21445590; Qin and Xiao, 2022b).
How does real estate income affect retirement age?: Lessons from
 national survey of tax and benefit*

In Western countries, where retirement funds primarily consist of pensions, research on retirement decision making revolves around pension-related considerations. Conversely, in Korea, where a whopping 77.5% of assets are tied to real estate, little attention has been devoted to exploring the connection between real estate holdings and retirement. This study employs panel models, utilizing data from the 2016-2021 National Survey of Tax and Benefit, to analyze the impact of real estate income on the expected retirement age. The empirical findings reveal that individuals with real estate income tend to postpone their retirement by an average of 2.25 years. Moreover, as both the amount of income derived from real estate and proportion of real estate income in total income increase, the incentives to continue working beyond the traditional retirement age strengthen notably. Given that this study is the first to establish a connection between real estate income and retirement, it is imperative to delve deeper into this topic and uncover further implications through subsequent research endeavors.
BCACP-IoE: A Novel Blockchain-Based Security Access Control Protocol for Internet of Energy

Thanks to the development of information technology, our energy system is gradually becoming connected and intelligent. The deep integration of the mature Internet of Things and various fields of energy forms the Internet of Energy (IoE), which can make an important contribution to improving energy efficiency and reducing carbon dioxide emissions. However, the continuous deepening of information technology in IoE, it also brings a lot of network security problems, because the essence of IoE information exchange is wireless communication. Such network security problems may result in the disclosure of private information of the power user side (PUS) to other unauthorized parties, or in the acquisition of trade secrets of the power supply side (PSS) by competitors. Therefore, we need to develop a specific data access protocol to ensure the communication security of IoE. This paper proposes a blockchain-based access control protocol for IoE, namely “BCACP-IoE”. As most grid companies (GC) are run by governments, BCACP-IoE treats them as trusted entities and designs secure access protocols for PSS/PUS-GC communication. Meanwhile, we use Ripple, a vote-based blockchain consensus algorithm, to validate transactions and add them to the blockchain. Through various security analyses, we prove the security of the BCACP-IoE against different types of potential attacks. In addition, it also has lower communication and computing costs, and its performance is better than other existing schemes.
Optimizing Variable Radius Plot Size and LiDAR Resolution to Model Standing Volume in Conifer Forests

Abstract The conventional approach to LiDAR-based forest inventory modeling depends on field sample data from fixed-radius plots (FRP). Because FRP sampling is cost intensive, combining variable-radius plot (VRP) sampling and LiDAR data has the potential to improve inventory efficiency. The overarching goal of this study was to evaluate the integration of LiDAR and VRP data. FRP and VRP plots using different basal-area factors (BAF) were colocated in 6 conifer stands near Alberta, Michigan, in the United States. A suite of LiDAR metrics was developed for 24 different resolutions at each plot location, and a number of nonparametric prediction models were evaluated to identify an optimal LiDAR resolution and an optimal scale of VRP to spatially extend the data. An FRP-based model had root mean square error (RMSE) of 31.8 m3 ha−1, whereas the top VRP-based models were somewhat less precise, with RMSE of 38.0 m3 ha−1 and 45.8 m3 ha−1 using BAF 2.06 m2 ha−1 and BAF 2.29 m2 ha−1, respectively. The optimal LiDAR resolution for the VRP data was found to be 18 m for the selected stands, and plot-level estimates based on a model using BAF 2.29 m2 ha−1 were statistically equivalent to the FRP measurements. The use of VRP data shows promise and can substitute for FRP measurements to improve efficiency.
Cybersecurity Cloud-Based Online Learning: A Literature Review Approach

Cloud-based online learning is the electronic learning activity that supports teaching and learning (T&L) that could be done from anywhere and in the world. Some of its benefits are scalability and affordability that could in a decision-making support on the mechanisms of material selection. Cloud computing has been adopted by most universities around the world. In this regard, lecturers and students will use it to facilitate T&L; however, due to concerns of information technology or systems security, cloud-based online learning users are also not immune. In this regard, the users could be affected by different cybersecurity attacks. In this paper, a systematic literature review method was used to sift the different models and solutions used to address the cybersecurity concerns surrounding cloud-based online learning. A brief Likert-scale questionnaire was used to obtained data that could corroborate the systematic literature findings. In this regard, a group of 20 online learning designers were sampled as participants. It was found that the confidentiality, integrity, and availability issues are a concern. This led to issues of security awareness, authentication and blended attacks being issues. In this regard, a cloud-based online learning model is not immune from security issues. In this paper, a conceptual framework as the line-of-defense is proposed as a solution towards having a cybersecure cloud based online learning.
Mixture Effects of Per- and Polyfluoroalkyl Substances on Embryonic and Larval Sheepshead Minnows (Cyprinodon variegatus)

Per- and polyfluoroalkyl substances (PFAS) are ubiquitous and persistent environmental contaminants originating from many everyday products. Perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) are two PFAS that are commonly found at high concentrations in aquatic environments. Both chemicals have previously been shown to be toxic to fish, as well as having complex and largely uncharacterized mixture effects. However, limited information is available on marine and estuarine species. In this study, embryonic and larval sheepshead minnows (Cyprinodon variegatus) were exposed to several PFAS mixtures to assess lethal and sublethal effects. PFOS alone was acutely toxic to larvae, with a 96 h LC50 of 1.97 mg/L (1.64–2.16). PFOS + PFOA resulted in a larval LC50 of 3.10 (2.62–3.79) mg/L, suggesting an antagonistic effect. These observations were supported by significant reductions in malondialdehyde (105% ± 3.25) and increases in reduced glutathione concentrations (43.8% ± 1.78) in PFOS + PFOA exposures compared to PFOS-only treatments, indicating reduced oxidative stress. While PFOA reduced PFOS-induced mortality (97.0% ± 3.03), perfluorohexanoic acid (PFHxA) and perfluorobutanoic acid (PFBA) did not. PFOS alone did not affect expression of peroxisome proliferator-activated receptor alpha (pparα) but significantly upregulated apolipoprotein A4 (apoa4) (112.4% ± 17.8), a downstream product of pparα, while none of the other individually tested PFAS affected apoa4 expression. These findings suggest that there are antagonistic interactions between PFOA and PFOS that may reduce mixture toxicity in larval sheepshead minnows through reduced oxidative stress. Elucidating mechanisms of toxicity and interactions between PFAS will aid environmental regulation and management of these ubiquitous pollutants.
Involvement of Protease-Activated Receptor2 Pleckstrin Homology Binding Domain in Ovarian Cancer: Expression in Fallopian Tubes and Drug Design

Studying primordial events in cancer is pivotal for identifying predictive molecular indicators and for targeted intervention. While the involvement of G-protein-coupled receptors (GPCRs) in cancer is growing, GPCR-based therapies are yet rare. Here, we demonstrate the overexpression of protease-activated receptor 2 (PAR2), a GPCR member in the fallopian tubes (FTs) of high-risk BRCA carriers as compared to null in healthy tissues of FT. FTs, the origin of ovarian cancer, are known to express genes of serous tubal intraepithelial carcinoma (STICs), a precursor lesion of high-grade serous carcinoma (HGSC). PAR2 expression in FTs may serve as an early prediction sensor for ovarian cancer. We show now that knocking down Par2 inhibits ovarian cancer peritoneal dissemination in vivo, pointing to the central role of PAR2. Previously we identified pleckstrin homology (PH) binding domains within PAR1,2&4 as critical sites for cancer-growth. These motifs associate with PH-signal proteins via launching a discrete signaling network in cancer. Subsequently, we selected a compound from a library of backbone cyclic peptides generated toward the PAR PH binding motif, namely the lead compound, Pc(4-4). Pc(4-4) binds to the PAR PH binding domain and blocks the association of PH-signal proteins, such as Akt or Etk/Bmx with PAR2. It attenuates PAR2 oncogenic activity. The potent inhibitory function of Pc(4-4) is demonstrated via inhibition of ovarian cancer peritoneal spread in mice. While the detection of PAR2 may serve as a predictor for ovarian cancer, the novel Pc(4-4) compound may serve as a powerful medicament in STICs and ovarian cancer. This is the first demonstration of the involvement of PAR PH binding motif signaling in ovarian cancer and Pc(4-4) as a potential therapy treatment.
Design of Enterprise Economic Data Secure Storage System Based on Fuzzy Association Algorithm

This study investigates the utilization of fuzzy association rule algorithms for the secure storage of enterprise economic data. The study applies the concept of fuzzy partitioning to the mining of industrial production process control datasets. Through fuzzy clustering, numerical data with continuous, high-dimensional, and strong real-time characteristics are partitioned into several fuzzy subsets, thereby facilitating the discovery of fuzzy association rules that align more closely with conventional thinking. The study concludes by emphasizing the significance of data as a core asset for enterprises and the role of storage systems as the last line of defense for data security protection. As informatization develops, enterprises are storing increasing amounts of data in computer storage systems. The study proposes the use of fuzzy association rule algorithms to assist in the analysis of fault information, thereby enabling equipment maintenance personnel to quickly identify the causes of equipment failures and promptly maintain faulty equipment.
Secure Adaptive Group Testing

Group Testing (GT) addresses the problem of identifying a small subset of defective items from a large population, by grouping items into as few test pools as possible. In Adaptive GT (AGT), outcomes of previous tests can influence the makeup of future tests. Using an information theoretic point of view, Aldridge 2012 showed that in the regime of a few defectives, adaptivity does not help much, as the number of tests required is essentially the same as for non-adaptive GT. Secure GT considers a scenario where there is an eavesdropper who may observe on average a fraction <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> of the tests results, yet should not be able to infer the status of the items. In the non-adaptive scenario, the number of tests required is <inline-formula> <tex-math notation="LaTeX">$1/(1-\delta)$ </tex-math></inline-formula> times the number of tests without the secrecy constraint. In this paper, we consider Secure Adaptive GT. Specifically, when during the makeup of the pools one has access to a private feedback link from the lab, of rate <inline-formula> <tex-math notation="LaTeX">$R_{f}$ </tex-math></inline-formula>. We prove that the number of tests required for both correct reconstruction at the legitimate lab, with high probability, and negligible mutual information at the eavesdropper is <inline-formula> <tex-math notation="LaTeX">$1/min\{1,1-\delta +R_{f}\}$ </tex-math></inline-formula> times the number of tests required with no secrecy constraint. Thus, unlike non-secure GT, where an adaptive algorithm has only a mild impact, under a security constraint it can significantly boost performance. A key insight is that not only the adaptive link should disregard the actual test results and simply send keys, these keys should be enhanced through a “secret sharing” scheme before usage. We derive sufficiency and necessity bounds that completely characterizes the Secure Adaptive GT capacity. Moreover, we consider additional models of Secure Adaptive GT, where we make a clear distinction between the lab performing the tests, and the doctor analyzing the results. Specifically, we consider curious but non-malicious, non-cooperating labs. Each lab gets a fraction <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> of pool-tests to perform. Yet, we want to keep each lab ignorant regarding the status of the items. In contrast, the doctor who gets all outcomes, should successfully decode. When there is a feedback from each lab, we show that even if a curious lab obviously sees its own feedback (i.e., it is locally-public to Eve), secure adaptive GT is still possible, and at a rate that can be equal to the one without a security constraint at all, by an application of the Leftover Hash Lemma, using the data of one lab to protect against another.
A novel hybrid grey-BCM approach in multi-criteria decision making: An application in OTT platform

Real world decision making is like a puzzle having complex, uncertain and vague information and this fact portray the wide range applicability of grey system theory in decision making procedure as grey system theory deals with the systems having information with uncertainty. In order to extend the base-criterion method to uncertain conditions, grey information may be a better way to solve a lot of multi-criteria decision-making problems. In this paper, we proposed a novel approach ‘grey base-criterion method’ (GBCM) based on the linguistic variables extended to the grey information. Weights of criteria have been calculated using GBCM. Numerical examples are illustrated and then the results are compared by the grey best-worst method (GBWM). Results of comparison show the high reliability of GBCM method with less consistency ratio over GBWM. A real case study of the fastest growing OTT (Over the Top) platforms in India has been taken to bestow the robustness of the proposed method.
Travel Time Estimation on Urban Arterials - a Real Time Aspect

ii List of Figures . viii List of Tables . xii Acknowledgements . xiii 1 Introduction . 1 1.1 Measure Travel Time on Signalized Urban Arterials . 1 1.2 Estimate Travel Time through Modeling . 3 1.3 Research Objectives . 3 1.4 Research Design . 4 1.5 Challenges Faced and Research Contributions . 7 1.6 Organization of Dissertation . 8 2 Literature Review . 9 2.1 Detection Technology. 9 2.1.1 Point Measurement of Performance. 9 2.1.2 Link Measurement of Performance . 14 2.1.3 Travel Time Measurement . 19 2.2 Queue Estimation Models . 27 2.2.1 Simple Input-Output Model . 28 2.2.2 Kalman Filter Model . 29 2.2.3 Shock Wave Model . 33 2.2. 4 Probabilistic Model . 37 2.2. 5 Other Models . 40 2.3 Delay Estimation Models . 41 2.3.1 Deterministic Queueing Model . 41 2.3.2 Shock Wave Model . 42 2.3.3 Steady-State Stochastic Model . 44 2.3. 4 Time-Dependent Stochastic Model . 45 2.3. 5 Comparison of Delay Models . 45 2.3. 6 HCM 2000 Model . 47 2.3. 7 Route Travel Time . 49 vi 2.3. 8 Other Delay Models . 50 3 Methodology . 53 3.1 Major Hypothesis . 53 3.1.1 Relationship between Volume, Speed and Occupancy . 54 3.1.2 Vehicle Queue Length Estimation . 55 3.1.3 Travel Time Estimation . 56 3.2 Data Needs . 57 3.3 Analysis Approach . 58 3.3.1 Definition of Detector Volume, Speed and Occupancy . 58 3.3.2 Vehicle Trajectory . 60 3.3.3 Time-Space Diagram . 63 3.3. 4 Generalized Definition of Flow, Density and Speed . 64 4 Data for Analysis . 67 4.1 NGSIM Peachtree Street Data . 67 4.1.1 Field Data . 67 4.1.2 Detector and Link Placement . 71 4.1.3 Detector and Link Data Processing . 75 4.2 Field Data Collected in New York City . 78 4.3 Metered On-ramp Data . 80 4.4 Sample Size . 81 5 Analysis of Flow, Density, Speed, and Occupancy . 82 5.1 Detector Volume, Speed and Time Occupancy . 82 5.2 Vehicles Crossing a Detector . 87 5.2.1 Speed . 93 5.2.2 Density . 95 5.2.3 Flow Rate . 98 5.3 Time-Space Regions for Individual Vehicles . 102 5.3.1 Flow Rate . 106 5.3.2 Speed . 110 5.3.3 Density . 110 5.4 Traffic Flow Intensity . 114 6 Queue Length and Travel Time along Signalized Link . 117 6.1 Input-Output Technique for Queue Estimation . 117 6.1.1 Cumulative Input-Output Diagram . 118 vii 6.1.2 Queueing in Time Space Diagram . 121 6.1.3 Vehicle Count Adjustment Using Time Occupancy . 123 6.1.4 Input-Output Technique Reset Mechanism . 129 6.2 Travel Time Estimation . 133 6.2.1 Comparison with NCHRP Project 3-79 Model . 137 6.2.2 Comparison with Little’s Law . 139 6.2.3 Close Look at Individual Vehicle Travel Time . 141 7 Conclusion . 143 7.1 Detector Volume, Speed and Time Occupancy . 143 7.2 Vehicle Queue Length. 145 7.3 Travel Time . 146 8 References . 148 9 Curriculum Vitae . 156
Artificial Intelligence Patrol System for Transmission Line UAV Based on Ultraviolet Discharge

The system is characterized by flexibility, good stability, easy manipulation, and real-time and accurate transmission. This paper designs a transmission line UAV AI inspection UV discharge system, which is composed of a mobile monitoring station, several UAV aircraft and background host computer. The system uses cameras, infrared devices, and other methods of automatic navigation or manual directional control to collect information on the transmission line and transmit it to the mobile monitoring station and the background host computer in real time. Technicians can grasp the operation of transmission lines in real time and eliminate operational defects and safety hazards in a timely manner.
Blue-emitting fluorophosphate phosphor to enhance color rendition of near-ultraviolet LED white light

This work presents a novel blue phosphor, Na2MgPO4F: Eu2+ (NMPF: Eu) for violet 405-nm light-emitting diode (LED) devices. The compound is synthesized via a simple one-step sintering process using readily available precursors. Notably, NMPF: Eu exhibits highly efficient and thermally stable blue emission when excited by violet light. The NMPF: Eu is applied to produce a white LED device driven by a 405 nm LED chip and Y3Al5O12: Ce3+ (YAG: Ce) yellow phosphor. The impacts of NMPF: Eu phosphors are investigated by varying their particle size while maintaining consistent doping concentrations. Impressively, the LED prototype displays a substantial reduction in blue light emission while generating white light with enhanced color rendering and luminous properties. These results highlight the suitability and potential of NMPF: Eu as a promising phosphor for widening violet LED applications, especially in generating white light perceptible to the human eyes.
Mathematical modelling of transmission dynamics of Marburg virus with effective quarantine approach

Marburg virus disease (MVD) is a severe infection with an extremely high fatality rate, spread through direct and indirect contact. Recently, there was an outbreak of the deadly MVD disease. We studied the transmission dynamics of MVD through the SEQIR model and determined the basic reproduction number, the local stability, and the global stability of the disease-free equilibrium. This study aims to mathematically model the transmission dynamics of MVD using an effective Quarantine Approach. Building upon existing research, the model introduces a quarantine compartment and analyses the disease-free equilibrium.The results indicate that quarantining exposed individuals effectively curb the transmission of MVD and prevents secondary infections. Additionally, the introduction of treatment in the model ensures the survival of patients. However, it is emphasized that further research is essential to develop specific treatments for MVD, as preventive measures alone may not be sufficient to control the disease's spread.