Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Neural Network-Based Robust Adaptive Formation Control for Cooperative Transport of a Payload Using Two Underactuated Quadcopters

Designing a controller for the cooperative transport of a payload using quadcopter-type unmanned aerial vehicles (UAVs) is a very challenging task in control theory because these vehicles are underactuated mechanical systems. This paper presents a novel robust adaptive formation control design for the cooperative transport of a suspended payload by ropes using two underactuated quadcopters in the presence of external disturbances and parametric uncertainties. The structure of the proposed controller is divided into two subsystems: fully actuated and underactuated. An integral sliding mode adaptive control strategy is proposed for the fully actuated subsystem, and for the underactuated subsystem, an adaptive control strategy based on the combination of Backstepping and sliding mode is proposed. Then, the control parameters of the sliding surfaces of both control subsystems are adaptively tuned by a neural network. In addition, to improve the robustness of the proposed controller, a disturbance observer is incorporated to estimate and compensate for the lumped disturbances. The asymptotic stability of the cooperative transport system is verified with the Lyapunov theorem. Finally, numerical simulations are performed in MATLAB/Simulink environment, and the results show that the proposed controller successfully transports the payload safely and without oscillations. Moreover, the desired formation pattern is maintained throughout the flight task, even with external disturbances and parametric uncertainties.
ISP: An Improved Slicing Pair Code for Skewed Slicing Floorplan

Floorplan is a rectangular dissection of a rectangle into number of small rectangles to fit rectangular electronics blocks. Floorplan is used for designing the layout of very-large-scale-integrated (VLSI) circuit. Slicing floorplans are one of the important classes of floorplans that are formed repetitively cutting floorplans horizontally or vertically. In nano meter era, as transistors density in chip is increasing day by day, therefore, it is essential to design a compact floorplan for VLSI circuit. In this paper, we propose an effective coding scheme for encoding a skewed tree corresponds to the slicing floorplan. The size of the new code in bits for encoding the tree is less than the prior encoding schemes such as Breadth First Search (BFS) and Slicing Pair (SP) codes. Experimental result on random instances of slicing trees for the proposed code shows 49% and 20% reduction in number of bits in average than the BFS and SP codes respectively.
Tadalafil Rescues the p.M325T Mutant of Best1 Chloride Channel

Bestrophin 1 (Best1) is a chloride channel that localises to the plasma membrane of retinal pigment epithelium (RPE) cells. Mutations in the BEST1 gene are associated with a group of untreatable inherited retinal dystrophies (IRDs) called bestrophinopathies, caused by protein instability and loss-of-function of the Best1 protein. 4PBA and 2-NOAA have been shown to rescue the function, expression, and localisation of Best1 mutants; however, it is of interest to find more potent analogues as the concentration of the drugs required is too high (2.5 mM) to be given therapeutically. A virtual docking model of the COPII Sec24a site, where 4PBA has been shown to bind, was generated and a library of 1416 FDA-approved compounds was screened at the site. The top binding compounds were tested in vitro in whole-cell patch-clamp experiments of HEK293T cells expressing mutant Best1. The application of 25 μM tadalafil resulted in full rescue of Cl− conductance, comparable to wild type Best1 levels, for p.M325T mutant Best1 but not for p.R141H or p.L234V mutants.
Channel-Aware Subcarrier Allocation for Hybrid NOMA/OFDMA-Based Bandlimited Multi-User VLC Systems

In this paper, we propose a novel channel-aware subcarrier allocation scheme for hybrid non-orthogonal multiple access/orthogonal frequency division multiplexing access (NOMA/OFDMA)-based bandlimited multi-user visible light communication (VLC) systems. In the proposed channel-aware subcarrier allocation scheme, low-frequency (LF) subcarriers are allocated to the user pairs with worse channel conditions, while high-frequency (HF) subcarriers are allocated to the user pairs with better channel conditions. By using the proposed channel-aware subcarrier allocation scheme, the low-pass (LP) frequency response-induced power difference and the diverse channel conditions-induced power difference can be efficiently mitigated. Simulation results show that, in a bandlimited four-user VLC system applying the proposed channel-aware subcarrier allocation scheme, a remarkable 3.3-dB transmitted signal-to-noise (SNR) reduction at an overall bit error rate (BER) of 10−3 can be obtained in comparison to the benchmark schemes.
VaR bounds for joint portfolios with dependence constraints

Abstract Based on a novel extension of classical Hoeffding-Fréchet bounds, we provide an upper VaR bound for joint risk portfolios with fixed marginal distributions and positive dependence information. The positive dependence information can be assumed to hold in the tails, in some central part, or on a general subset of the domain of the distribution function of a risk portfolio. The newly provided VaR bound can be interpreted as a comonotonic VaR computed at a distorted confidence level and its quality is illustrated in a series of examples of practical interest.
Analysing Cyber Security Vulnerabilities using Click Jacking and HostHeader Injection

One of the most trending research topics of the rapidly developing digital world is cyber security. Today, the biggest concern facing businesses is digital security. A vulnerability is a flaw or mistake in a system or device's code that, if used improperly, could affect the confidentiality, accessibility, and integrity of any data stored in that system. Finding and analyzing the typical cyber security vulnerabilities like ClickJacking, Host Header Injection, Port Scanning, and Reverse IP is the aim of this study.
Research of integrating prior knowledge into abnormal behavior recognition model of EV charging station

In recent years, the number of EV (Electric Vehicle) charging stations has surged, the safety literacy of charging personnel is uneven. However, the number of station O&M personnel is limited, which seriously affects the safe and efficient operation of EV charging stations. The AI algorithms can be used to detect the abnormal behavior happening in the station and alarm to intervene in time. Existing AI algorithms face the challenge of small sample size and many types of abnormal behaviors during the training process, which leads to poor generalization of the neural network model and large detection error. This study attempts to integrate prior knowledge into the abnormal behavior recognition model based on neural network, so as to improve the accuracy of the algorithm. The field data verification shows that the performance of the proposed algorithm is improved compared with the traditional algorithm, and the problem of small samples and low accuracy of abnormal behavior detection is effectively overcome.
Long-term Power Generation Forecasting Method Based on BAS-BP Neural Network

In the context of global warming, the issue of carbon neutrality is being paid more attention. As one of the main sources of carbon emissions, the demand and supply of electricity can be precisely controlled by exploring the trend of power generation. Adequate forecasting of future power generation has far-reaching implications for the development and implementation of emission reduction measures in the power industry. However, the current research on power generation is mainly focused on traditional statistical models, and the forecasting accuracy of forecasting models is low and influenced by other objective factors. In order to improve the accuracy of electricity generation prediction models, this paper introduces an optimized prediction model based on BAS-BP neural network and conducts an empirical study using the national power generation data in India. The results show that the MAPE of the BAS-BP neural network is 0.006661, which is about 68.14% less than the error of pure BP model, about 92.29% less than the KNN model, and about 78.34% and 68.21% less than the LSTM and LSTM-SSA models respectively. It shows that the model is able to achieve accurate prediction of future fluctuations in electricity generation, providing a new and feasible method for future electricity forecasting.
A Review of the Preparation, Characterization, and Applications of Chitosan Nanoparticles in Nanomedicine

Chitosan is a fibrous compound derived from chitin, which is the second most abundant natural polysaccharide and is produced by crustaceans, including crabs, shrimps, and lobsters. Chitosan has all of the important medicinal properties, including biocompatibility, biodegradability, and hydrophilicity, and it is relatively nontoxic and cationic in nature. Chitosan nanoparticles are particularly useful due to their small size, providing a large surface-to-volume ratio, and physicochemical properties that may differ from that of their bulk counterparts; thus, chitosan nanoparticles (CNPs) are widely used in biomedical applications and, particularly, as contrast agents for medical imaging and as vehicles for drug and gene delivery into tumors. Because CNPs are formed from a natural biopolymer, they can readily be functionalized with drugs, RNA, DNA, and other molecules to target a desired result in vivo. Furthermore, chitosan is approved by the United States Food and Drug Administration as being Generally Recognized as Safe (GRAS). This paper reviews the structural characteristics and various synthesis methods used to produce chitosan nanoparticles and nanostructures, such as ionic gelation, microemulsion, polyelectrolyte complexing, emulsification solvent diffusion, and the reverse micellar method. Various characterization techniques and analyses are also discussed. In addition, we review drug delivery applications of chitosan nanoparticles, including for ocular, oral, pulmonary, nasal, and vaginal methodologies, and applications in cancer therapy and tissue engineering.
A Novel Curved-Coil Transmitter for Quasi-omnidirectional Wireless Power Transfer

Achieving stable power transfer by merely relying on quasi-omnidirectional couplers is challenging. In this paper, we propose a quasi-omnidirectional wireless power transfer (QWPT) system with a novel curved-coil transmitter to achieve steady transmission performance. A single power source is used to drive the transmitter's current without using a phase and current control methodology. Power is transmitted to the receiver through magnetic resonant coupling at a distance of 50 mm. Moreover, an equivalent circuit model of the curved-coil system is derived and mathematically analyzed. The mutual inductance of the proposed QWPT system is evaluated through analysis and experiments. The experimental results for the resonant coupling system confirm the theoretical analysis of the performance of the curved-coil transmitter and quasi-omnidirectional power transfer.
On a Linear Combination of Zagreb Indices

The modiﬁed ﬁrst Zagreb connection index of a triangle-free and quadrangle-free graph G is equal to 2 M 2 ( G ) − M 1 ( G ) , where M 2 ( G ) and M 1 ( G ) are the well-known second and ﬁrst Zagreb indices of G , respectively. This paper involves the study of the linear combination 2 M 2 ( G ) − M 1 ( G ) of M 2 ( G ) and M 1 ( G ) when G is a connected graph of a given order and cyclomatic number. More precisely, graphs having the minimum value of the graph invariant 2 M 2 − M 1 are determined from the class of all connected graphs of order n and cyclomatic number c y , when c y ≥ 1 and n ≥ 2( c y − 1) .
Hybrid Argon Plasma Coagulation for Barrett’s Esophagus and for Colonic Mucosal Resection—A Systematic Review and Meta-Analysis

Hybrid argon plasma coagulation (hAPC) is a novel technique that combines conventional argon plasma coagulation and waterjet submucosal expansion. The aims of this metanalysis were to evaluate the efficacy and safety of hAPC in the setting of Barret’s esophagus (BE) ablation and as an adjunct to colonic endoscopic mucosal resection (EMR). Four electronic databases were searched, and the results were analyzed by two independent authors. Random-effects meta-analyses of the proportions of endoscopic and histologic remission (for BE), recurrence, and post-procedure adverse events were performed using R. Studies’ reporting quality was also assessed. From the 979 identified records, 13 studies were included (10 regarding BE and three colonic EMR). The pooled percentages of endoscopic and histologic remission after hAPC for BE were 95% (95% confidence interval [CI] 91–99, I2 = 34) and 90% (95%CI 84–95, I2 = 46), respectively, while major adverse events and recurrence were registered in 2% (95%CI 0–5, I2 = 41) and 11% (95%CI 2–27, I2 = 11), respectively. Concerning hAPC-assisted EMR, the pooled percentages of major adverse events and recurrence were 5% (95%CI 2–10, I2 = 0) and 1% (95%CI 0–3, I2 = 40). Evidence suggests that the main advantages of hAPC are the increase in safety in the setting of BE ablation and the reduction of local recurrence after colonic EMR. Trials comparing hAPC with standard strategies are required to support its use for these indications.
Strategies for Surface Design in Surface Plasmon Resonance (SPR) Sensing

Surface plasmon resonance (SPR) comprises several surface-sensitive techniques that enable the trace and ultra-trace detection of various analytes through affinity pairing. Although enabling label-free, sensitive detection and real-time monitoring, several issues remain to be addressed, such as poor stability, non-specific adsorption and the loss of operational activity of biomolecules. In this review, the progress over sensor modification, immobilization techniques and novel 2D nanomaterials, gold nanostructures and magnetic nanoparticles for signal amplification is discussed. The advantages and disadvantages of each design strategy will be provided together with some of the recent achievements.
Systematic Social Observation at Scale: Using Crowdsourcing and Computer Vision to Measure Visible Neighborhood Conditions

Analysis of neighborhood environments is important for understanding inequality. Few studies, however, use direct measures of the visible characteristics of neighborhood conditions, despite their theorized importance in shaping individual and community well-being, because collecting data on the physical conditions of places across neighborhoods and cities and over time has required extensive time and labor. The authors introduce systematic social observation at scale (SSO@S), a pipeline for using visual data, crowdsourcing, and computer vision to identify visible characteristics of neighborhoods at a large scale. The authors implement SSO@S on millions of street-level images across three physically distinct cities—Boston, Detroit, and Los Angeles—from 2007 to 2020 to identify trash across space and over time. The authors evaluate the extent to which this approach can be used to assist with systematic coding of street-level imagery through cross-validation and out-of-sample validation, class-activation mapping, and comparisons with other sources of observed neighborhood characteristics. The SSO@S approach produces estimates with high reliability that correlate with some expected demographic characteristics but not others, depending on the city. The authors conclude with an assessment of this approach for measuring visible characteristics of neighborhoods and the implications for methods and research.
Risk and Weightage Based Route Prediction System Using Machine Learning: A Review

Transportation is the key context ingredient in modern life. Optimised route prediction is the vital aspect for commercial enterprises and individuals also. Proper route prediction not only helps to save time, fuel, and money, it additionally prevents accident, air pollution. Operational efficiency like uncertain event and incident can be cause for delay delivery, reaching late in destination that cause also the revenue loss for organisation and individual also. According to the 2019 Urban Mobility Report in 2017, congestion caused urban Americans to travel an extra 8.8 billion hours and purchase an extra 3.3 billion gallons of fuel for a congestion cost of $166 billion. Trucks account for $21 billion (12 percent) of the cost, much more than their 7 percent of traffic. The average auto commuter spends 54 hours in congestion and wastes 21 gallons of fuel due to congestion at a cost of $1,010 in wasted time and fuel. [1]. Nowadays drivers are using different Map software and GPS(Global Positioning System) to choose the appropriate route but sudden changes like climate variation, road congestion due to social gathering, road construction work etc. are difficult to incorporate in existing map software. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data has become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, Machine learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. Machine Learning models are able to predict more accurate rote in transportation systems. This paper deeply analyses existing route prediction systems and different technology used in those systems. The Authors will focus on improving this route prediction using a machine learning model.
Fecal Metabolomics Reveals the Foraging Strategies of Giant Pandas for Different Parts of Bamboo

Simple Summary Climate change can have a negative impact on the food sources of wild animals, including giant pandas, who primarily rely on bamboo. To better understand the foraging strategy of giant pandas and aid in their conservation, metabolomic methods were used to analyze fecal metabolites and correlate them with gut microbiota. The results indicate that the sugar content in giant panda feces is higher when they consume bamboo culm (the hollow, jointed stem) with high fiber content and that Streptococcus bacteria are positively correlated with glucose and acetic acid content, both of which are related to fiber. These results suggest that the foraging strategy of giant pandas is based on the nutritional composition of different bamboo parts, and therefore, enriching their habitat with bamboo species is essential to achieve their natural foraging strategy to obtain adequate nutrition. Abstract Climate change-induced food shortages pose major threats to wildlife conservation, and the exclusive reliance of giant pandas on bamboo makes them particularly vulnerable. The aim of this study was to provide insight into the reasons for the foraging strategies of giant pandas to selectively forage for different bamboo parts (bamboo shoot, culm, and leaf) during different seasons. This study used a metabolomic approach to analyze the fecal metabolites of giant pandas and conducted a correlation analysis with their gut microbiota. The results indicate that the fecal metabolites of giant pandas differ significantly depending on the bamboo parts they forage on, with higher sugar content observed when they consume bamboo culm with high fiber content. By functional annotation, culm group metabolites were enriched in the galactose metabolic pathway, while shoot group metabolites were enriched in the phenylalanine, tyrosine and tryptophan biosynthesis pathways. Moreover, Streptococcus showed a significant positive correlation with glucose and acetic acid content. Therefore, the foraging strategy of giant pandas is based on the ability to utilize the nutrient content of different bamboo parts. Captive feeding and habitat construction should enrich bamboo species to allow them to express their natural foraging strategies and improve their welfare and reproductive status.
Wideband Shared-Radiator Four-Element MIMO Antenna Module for 5G Mobile Terminals

A low-profile wideband shared-radiator four-element multiple-input multiple-output (MIMO) antenna module that can be directly and flexibly integrated with the chassis ground of a mobile terminal is proposed. The module structure of a grounded square shared patch surrounded by four corner-cut patches is constructed through the characteristic mode analysis (CMA). By analyzing the low efficiency problem caused by the reverse currents, the off-diagonal feed positions are proposed to enhance the efficiency. The use of the shared patch with four I-shaped slots and the off-diagonal feeds makes the dimension of the proposed four-element antenna module only $30\times 30\times $ 2 mm3. With the help of multiple regionalized characteristic currents and four grounded short branches, the proposed antenna module shows good simulated isolations of better than 12.5 dB in the −6 dB impedance operating band of 4.25–5.13 GHz. To verify the integration flexibility, an $8\times $ 8 MIMO antenna prototype with two proposed modules directly deployed on a phone-size chassis ground was fabricated and measured. The experimental results indicate that the −6 dB impedance operating band can cover the fifth-generation (5G) N79 band (4.4–5 GHz) with isolations of better than 10 dB and efficiencies of 40.3%–48.5%. The diversity performance, the channel capacity, the effects of the user’s hands and head on the antenna performance, and the specific absorption rate (SAR) are analyzed, and the results are acceptable. With the advantages of zero clearance, small size, wide bandwidth, isolation adjustment flexibility, and integration flexibility, the proposed antenna module is potentially applicable in future mobile terminals.
Antenna Selections Strategies for Massive MIMO Systems With Limited-Resolution ADCs/DACs

In millimeter wave (mmWave) communication systems with massive multiple-input multiple-output (MIMO) architecture, selecting the antennas contributing most from the candidate array to transmit/receive signals is one of the effective solutions to reduce hardware cost and power consumption while maintaining high spectral efficiency. In this paper, for the communication systems where the base station (BS) equipped with massive MIMO antenna array communicates with multiple single-antenna users, the impact of limited-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) on system capacity is investigated, and two antenna selection (AS) algorithms, namely quantization-aware greedy with square maximum-volume (QAG-SMV) and group-selection (GS) schemes, are proposed to enhance system capacity for the uplink and downlink transmission, respectively. Specifically, after the quantization noise caused by limited-resolution ADCs/DACs is converted to independent additive noise, the problem of maximizing system capacity is formulated. Then, two novel AS schemes are proposed to improve system capacity. Simulation results show that the proposed AS algorithms can obtain higher average system capacity, and the computational complexity is reduced as well.