Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Urban Impervious Surfaces Mapping through Integrating Statistical Methods and Spectral Mixture Analysis

Impervious surfaces have been widely considered as the key indicator for evaluating urbanization and environmental quality. As one of the most widely applied methods, spectral mixture analysis (SMA) has been commonly used for mapping urban impervious surface fractions. When implementing SMA, the original multispectral remote-sensing reflectance images are served as the foundation and key to successful SMA. However, the limited spectral variances among different land covers from the original reflectance images make it challenging in information extraction and results in unsatisfactory mapping results. To address this issue, a new method has been proposed in this study to improve urban impervious surface mapping through integrating statistical methods and SMA. In particular, two traditional statistical methods, principal component analysis (PCA) and minimum noise fraction rotation (MNF) were applied to highlight the spectral variances among different land covers. Three endmember classes (impervious surface, soil, and vegetation) and corresponding spectra were identified and extracted from the vertices of the 2-D space plots generated by the first three components of each of the statistical analysis methods, PCA and MNF. A new dataset was generated by stacking the first three components of the PCA and MNF (in a total of six components), and a fully constrained linear SMA was implemented to map the fractional impervious surfaces. Results indicate that a promising performance has been achieved by the proposed new method with the systematic error (SE) of −3.45% and mean absolute error (MAE) of 11.52%. Comparative analysis results also show a much better performance achieved by the proposed statistical method-based SMA than the conventional SMA.
Energy flow control in a modular DC-DC converter with energy recovery

This paper proposes an energy management control scheme for controlling the energy flow in a modular and scalable DC/DC converter design with energy recovery, based on a fundamental building block structure. The overall design in terms of control, number and configuration of the building blocks for optimised electrical operation is presented. Series and parallel connection of a low voltage and current building block are used to achieve the required current and voltage. This flexibility allows for a converter design with different operating characteristics and enhanced degree of reconfigurability, while keeping the same design of the fundamental building blocks. This combination results in a more scalable and flexible converter for efficient maintenance, with modularised storage for increased safety. In addition, a strategy of redundancy is implemented, which gives increased up-time and enhances the possibility for hot-swapping.
Reduced Grid Representation through Proper Orthogonal Decomposition

This paper presents the benefits of using Proper Orthogonal Decomposition (POD) to build reduced representations of static grids to be used for market and planning studies. A simple grid reduction based on POD is shown to yield more accurate results than traditional network equivalents due to a combination of zonal dimensional reductions and a proper criteria for the allocation of a prescribed degree of freedom among potential variables.
Biomedical Text Similarity Evaluation Using Attention Mechanism and Siamese Neural Network

It is a crucial component to estimate the similarity of biomedical sentence pair. Siamese neural network (SNN) can achieve better performance for non-biomedical corpora. However, SNN alone cannot obtain satisfactory biomedical text similarity evaluation results due to syntactic complexity and long sentences. In this paper, a cross self-attention (CSA) is proposed to design a new attention mechanism, namely self2self-attention(S2SA). Then the S2SA is introduced into SNN to construct a novel self2self-attentive siamese neural network, namely S2SA-SNN. In the S2SA-SNN, self-attention is used to learn the different weights of words and complex syntactic features in a single sentence. The means of the CSA are used to learn inherent interactive semantic information between sentences, and it employs self-attention instead of global attention to perform cross attention between sentences. Finally, three biomedical benchmark datasets of Pearson Correlation of 0.66 and 0.72/0.66 on DBMI and CDD-ful/-ref are used to test and prove the effectiveness of the S2SA-SNN. The experiment results show that the S2SA-SNN can achieve better performances with pre-trained word embedding and obtain better generalization ability than other compared methods.
ITO Interface Modifiers Can Improve VOC in Polymer Solar Cells and Suppress Surface Recombination

We use dipolar phosphonic acid self-assembled monolayers (PA SAMs) to modify the work function of the hole-extracting contact in polymer/fullerene bulk heterojunction solar cells. We observe a linear dependence of the open-circuit voltage (VOC) of these organic photovoltaic devices on the modified indium tin oxide (ITO) work function when using a donor polymer with a deep-lying ionization energy. With specific SAMs, we can obtain VOC values exceeding those obtained with the common poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) hole-extraction layer. We measure charge-carrier lifetimes and densities using transient photovoltage and charge extraction in a series of devices with SAM-modified contacts. As expected, these measurements show systematically longer carrier lifetimes in devices with higher VOC values; however, the trends provide useful distinctions between different hypotheses of how transient photovoltage decays might be controlled by surface chemistry. We interpret our result.
MobileSPR App and Case: A Combined Logical and Mechanical Solution for Surface Plasmon Resonance Sensors Based on Smartphone

This paper proposes a mechanical structure (MobileSPR Case) combined with mobile application (MobileSPR App) to provide a complete solution for surface plasmon resonance (SPR) sensor based on smartphones. The mechanical structure was specially designed to hold all optical elements on the smartphone to archive the surface plasmon excitation. Manufactured in a 3D printer, the case fit from the top, and is adjustable for several frontal camera dispositions. Composed of two parts, makes the smartphone replacement process easier, as well enable thin- and thicker- biochips for surface plasmon excitation. The embedded application controls light source, camera, data process, information displayed and results storage, avoid an extra components to create a portable SPR solution. The App manipulate the light beams provided by the smartphone screen, varying the color between 380 nm to 780 nm, allowing the usage with biochips of different materials, metal layer and configurations. To demonstrates the solution feasibility a Xiaomi MI 9 Lite smartphone was used. Both a flat (thin) and trapezoidal (thicker) biochips operating in the angular interrogation were attached in the MobileSPR Case & App solution, and aqueous substances were exploited experimentally.
COUMARINS FROM MURRAYA PANICULATA (RUTACEAE)

Phytochemical study on leaves of Murraya paniculata have yielded four coumarins of auraptene(1), transgleinadien (2), 5,7-dimethoxy-8-(3-methyl-2-oxo-butyl)coumarin (3) and toddalenone(4). The compounds were isolated using chromatographic methods and identified using spectroscopic techniques. Antimicrobial activity evaluation on the crude extracts and pure compounds indicated chloroform extracts of the leaves exhibited moderate activity and only gleinadiene (2)showed moderate activity against Bacillus cereus. trans Isomer of Gleinadiene (2) has never been reported from this plant previously.
Estimation of Voltage Unbalance at the Reference Bus in Distribution System State Estimation

Distribution System State Estimation provides an accurate model to assess the real-time condition of the three-phase unbalanced distribution networks. However, the entire estimation is prone to the voltage unbalance at the reference bus (RB), typically assumed with balanced voltage phase angles at the substation. This work enhances the voltage unbalance estimation for traditional three-phase state estimators and demonstrates that many practical situations enable proper estimation of the RB voltage unbalance without the need for special substation models or Phasor Measurement Units (PMUs). A three-phase numerical observability analysis, based on the H∆ matrix, assesses the feasibility of estimating the voltage phase angles at the RB through the coupling elements among different phases. Simulation results on the IEEE test feeders demonstrate the feasibility and the effects of a proper voltage unbalance estimation, showing an overall increased accuracy for the entire network.
In-Band Full-Duplex Dynamic Spectrum Sharing in Beyond 5G Networks

Dynamic spectrum sharing (DSS) has recently attracted keen interest from regulatory bodies around the world as a key strategy to overcome spectrum scarcity in beyond 5G networks. In the United States, the Federal Communications Commission has adopted a three-tier DSS model for the Citizens Broadband Radio Service (CBRS) band managed by a spectrum access system (SAS) to share incumbents' spectrum with commercial cellular broadband applications. In this article, we first present a survey of current DSS approaches and their limitations and then discuss the benefits of using in-band full-duplex (IBFD) techniques in DSS networks. To illustrate the improvement in the performance of priority access license (PAL) and general authorized access (GAA) users in an IBFD-assisted CBRS, we consider a CBRS mobile broadband network (MBN) architecture comprising an incumbent in the form of a MIMO radar system and an IBFD MIMO MBN, consisting of PAL and GAA users. In particular, we design joint beamformers at the MBN, with constraints on transmit power at the MBN and detection probability of the radar system, and beamformers at the radar system to mitigate the interference from the radar system toward the cellular system. It is shown that the IBFD CBRS network architecture not only leads to improved performance of PAL and GAA users, but does so while producing less interference toward the radar than state-of-the-art half-duplex solutions. Finally, we present some open research challenges to invigorate research on beyond 5G IBFD DSS networks.
Experimental Investigation of Visible Light Communications Coverage in Vehicle-to-Vehicle Applications

Communication-based vehicle safety applications have the potential to take traffic safety to the next level. This type of application envisions that vehicles on the road exchange traffic safety relevant data in order to cooperate toward traffic accident prevention, improved traffic efficiency and better driving experience. In this context, this paper provides an analysis of the vehicle-to-vehicle coverage in a two-lane driving scenario for the visible light communication (VLC) technology. Thus, it provides the experimental evaluation of this coverage range using a VLC prototype based on standard LED vehicle rear lights. The experimental results demonstrate a communication range of at least 50 meters, with an initial 2-8 meters region not covered by the receiver’s FOV, when the vehicles are on adjacent lanes. The analysis of bit error rate results shows that even with an adequate signal processing, the number of errors can increase by about two order of magnitude when the vehicles are located on neighboring lanes.
Intraday Increasing Block Pricing for the Residential Electricity Sector

Designing pricing schemes for the electricity retail market is an emerging area with significant impact on the smart grid. This paper proposes the Intraday Increasing Block Pricing scheme, where charging is based on intraday pricing periods. The electricity consumed in each pricing period is split into consumption-blocks with increasing prices. The goal of consumers is to reduce their consumption within each pricing period by distributing their load throughout the day to avoid higher prices. In addition to explaining the proposed pricing scheme, the paper models the retail market behaviour when considering rational consumers. The resulting model is a convex optimization problem that can be utilized as a tool to evaluate the impact of the proposed pricing scheme on consumers and the power system. Different case studies demonstrate the consumer interaction with the proposed scheme and provide evidence on the improvement of power system efficiency, consumer surplus and social equity promotion.
LiDAR SLAM With Plane Adjustment for Indoor Environment

Planes ubiquitously exist in the indoor environment. This letter presents a real-time and low-drift LiDAR SLAM system using planes as the landmark for the indoor environment. Our algorithm includes three components: localization, local mapping and global mapping. The localization component performs real-time and global registration, instead of the scan-to-scan registration adopted in the state-of-the-art LiDAR odometry and mapping (LOAM) framework that yields lower fidelity poses. The local mapping component optimizes poses of the keyframes within a sliding window and parameters of the planes observed by these keyframes. The global mapping component conducts global plane adjustment (GPA) that jointly refines plane parameters and keyframe poses. The GPA is triggered when planes are revisited, rather than a place is revisited. This can establish constraints among remote places, and correct the drift without having to go back to a previously visited place. We adopt the point-to-plane distance to construct the cost functions of all the three components. Although this distance results in a large-scale least-squares problem that seems not suitable for real-time applications, we propose efficient algorithms to solve the resulting minimization problems by exploiting the special structure of the point-to-plane distance. Experimental results show that our algorithm achieves real-time performance and outperforms state-of-the-art LiDAR SLAM algorithms.
Enhanced Wasserstein Distributionally Robust OPF With Dependence Structure and Support Information

This paper goes beyond the current state of the art related to Wasserstein distributionally robust optimal power flow problems, by adding dependence structure (correlation) and support information. In view of the space-time dependencies pertaining to the stochastic renewable power generation uncertainty, we apply a moment-metric-based distributionally robust optimization, which includes a constraint on the second-order moment of uncertainty. Aiming at further excluding unrealistic probability distributions from our proposed decision-making model, we enhance it by adding support information. We reformulate our proposed model, resulting in a semi-definite program, and show its satisfactory performance in terms of the operational results achieved and the computational time.
Nanospark at the interface between organic solvents and tin-doped indium oxide

A nanometer-scale surface modification of tin-doped indium oxide (ITO) in organic solvent was performed using a scanning tunneling microscope (STM). The surface modification was attributed to localized field evaporation upon applying 10 V between the tunneling gap. White light sparking beneath the STM tip (nanospark) was observed during and after the surface modification. The addition of ionic species into the otherwise pure solvent also induced a nanospark even when applying lower voltages, but in this case, surface modification did not occur. Thus, it is proposed that a part of the ITO becomes dissolved in solution as ions, which results in electron avalanche in the presence of these induced ions.
Demand Side Power Management and Voltage Stability of an Autonomous Microgrid Using Atom Search Optimization

An autonomous microgrid integrated with renewable energy sources has a big challenge to fulfill the load demand all the time in a day. Especially, solar energy-based dish-Stirling power generator needs a supplementary source of energy for uninterrupted continuous power supply. The battery energy system may be a support system to the solar dish-Stirling arrangement for continuous power supply. This is the proposed system for demand-side power management and voltage stability. The classical PI controller is adopted for easiness of the controller design strategy and its implementation. The DC bus voltage of the autonomous microgrid is maintained during demand-side power management. A newly developed metaheuristic algorithm Atom Search Optimization (ASO) is explored to achieve the objective of the proposed work. The results obtained are compared with the results obtained using the particle swarm optimization technique, and it is found that the ASO gives better performance. The proposed work is implemented and simulated on the MATLAB/Simulink platform.
A M/M/ queue in a semi-Markovian environment

We consider an M/M/1 queue in a semi-Markovian environment. The environment is modeled by a two-state semi-Markov process with arbitrary sojourn time distributions <i>F</i><inf>0</inf>(<i>x</i>) and <i>F</i><inf>1</inf>(<i>x</i>). When in state <i>i</i> = 0, 1, customers are generated according to a Poisson process with intensity &lambda;<inf><i>i</i></inf> and customers are served according to an exponential distribution with rate &mu;<inf><i>i</i></inf>. Using the theory of Riemann-Hilbert boundary value problems we compute the <i>z</i>-transform of the queue-length distribution when either <i>F</i><inf>0</inf>(<i>x</i>) or <i>F</i><inf>1</inf>(<i>x</i>) has a rational Laplace-Stieltjes transform and the other may be a general --- possibly heavy-tailed --- distribution. The arrival process can be used to model bursty traffic and/or traffic exhibiting long-range dependence, a situation which is commonly encountered in networking. The closed-form results lend themselves for numerical evaluation of performance measures, in particular the mean queue-length.
Therapeutic Potential of Bamboo Shoots against Cancer: An Overview

Cancer, the feared non-communicable disease, contributes a staggering 16% to total deaths worldwide. Despite the enormous advances in medical research, cancer continues to pose an impregnable challenge for global health care systems with its massive economic burden mounting to approximately US$ 1.16 trillion. However, about 30-50% cases of cancer remain preventable mainly through modification of our dietary patterns. Over the years it has been scientifically proven that plant based diet exert preventive and antagonistic effects on cancer incidences. These whole plants and other natural plant products have a plethora of bioactive phytochemicals embedded in them which actively functions towards prevention and treatment of varied cancer forms. Moreover, plant based cancer prevention therapy is considered safe largely because it lacks the side effects normally associated with other cancer treatment strategies like chemotherapy and radio therapy etc. Bamboo shoots, the highly palatable juvenile culms of bamboo plant which have proven nutritional credentials, possess rich repositories of several phytochemicals like phenolic compounds, dietary fibres, phytosterols and several other functional groups which are known to possess anti-cancerous properties. With its worldwide distribution, bamboo shoots represent a vast natural resource which can very effectively be promoted as a potential functional food in the form of a highly efficient, healthy and economic anticancer therapeutic system.
SYMBOLIC DATA APPROACH FOR FRAUD DETECTION IN HEALTHCARE SYSTEM

— Fraud and abuse in the health care system have become a significant concern and that too inside health insurance organizations. In the last decade due to expanding misfortunes in incomes, handling medical claims have become a debilitating manual assignment, which is done by a couple of clinical specialists who have the duty of endorsing, adjusting, or dismissing the appropriations mentioned inside a restricted period from their gathering. Standard data mining techniques at this point do not sufficiently address the intricacy of the world. In this way, utilizing Symbolic Data Analysis is another sort of data analysis that permits us to address the intricacy of the real world and to recognize fraud in the healthcare data. In the era of digitization, the frauds are found in all categories of health insurance. It is finished next to deliberate trickiness or fraud for acquiring some pitiful advantage in the form of health expenditures. Bigdata analysis can be utilized to recognize fraud in large sets of insurance claim data. In light of a couple of cases that are known or suspected to be false, the fraud detection technique computes the closeness of each record to be fake by investigating the previous insurance claims. The investigators would then be able to have a nearer examination for the cases that have been set apart by data mining programming. One of the issues in healthcare system claims is the abuse of the medical insurance. Manual detection of frauds in the healthcare industry is strenuous work. In the proposed work the symbolic data object representation reduces the complexity of the medical data and machine learning and symbolic data analysis recognize the fraud detection. The symbolic data approach for fraud detection in healthcare system achieves promising results and can be extended to other healthcare systems
Building Penetration Loss for GSM Signals into Selected Building Structures in Jigawa State, Nigeria

Global system for mobile communication (GSM) became the most active industry in our country Nigeria, since its inception in 2004. The number of service providers keeps on increasing, but the quality of services (QoS), provided is still low. This research work, studied by way of extensive measurements, the received signal strength inside buildings constructed using different types of building materials for both the structure and the roof. This is with a view to finding a signal attenuation margin that will improve the indoor propagation model. The degree of attenuation on the received signal may be used to provide a margin in GSM radio link planning so as to compensate for such loss. The measurements were conducted in selected areas in Jigawa metropolis where these types of buildings are common. Four geographical locations were used as case study within Jigawa metropolis namely, Dutse, Kazaure, Hadejia, Gumel and Ringim. Signals radiated from four GSM service providers (namely, MTN, ETISALAT, Glo and Airtel) were used as basis for the measurements. Spectrum analyzer mounted on a trolley and controlled by a PC was used for this work; measurements on straight routes in corridors, rooms and the balcony were performed with equidistant measurements points. Several measurements campaigns with different location at 230 and 1500MHz were undertaken. Based on the results obtained, we conclude that, among the combination of building materials studied in this research mud building/rusty zinc accounted for higher signal losses while sandcrete building/good zinc roof accounted for lower signal losses for all the network providers considered in this study.
Reduction Kinetics of Thermally Reduced Graphene Oxide Thin Films

We report an experimentalstudy of thermal reduction of graphene oxide (GO) under nitrogen flow.Ultrathin films of GO were prepared on quartz glass substrates by spin-coatingfrom 3 mg/ml aqueous solutions of GO. These films were annealed at 50 °C under vacuum overnight before thermal reduction.The films were exposed to temperatures of 100 °C, 150 °C, 200 °C, 300 °C, 400 °C, 500 °C and 800 °C under nitrogen flowing. We used UV-Vis absorption spectroscopy tomonitor the change of absorption spectra caused by thermal reduction. We observed a significant change ofabsorption spectra due to formation of reduced graphene oxide (RGO). We found that thermal reduction startedalready at 100 °C, but proceeded significantly faster at heating temperatures higherthan 200 °C.