Miracle-dz/UniFAR-FTUs huggingface dataset:
Laser-Based Control of Rotary-Wing UAVs

Unmanned Aerial Vehicles (UAVs) present a high technological development rate nowadays. These vehicles can be used to perform dangerous and costly inspection procedures in structures with difficult access instead of human operators, but they still need a close monitoring. This thesis addresses the problem of using exclusively sensors on board UAVs to derive attitude determination tools and trajectory tracking strategies. Firstly, this work discusses the perception of the outside world by the vehicle and the formulation of a mathematical description containing information regarding its position and attitude relative to the structure. For this purpose, a geometry is set and the best fit to the data provided by a LiDAR sensor is selected, after a robust outlier filtering process. With this information, several methods for obtaining the attitude are proposed. These include a fast and comprehensive yaw estimator, based on continuity, and a closed-form solution for the Wahba's problem and a nonlinear filter for a full attitude estimation, both on the group of rotation matrices. A significant effort was devoted to the analysis of the entire procedure through simulation, from the creation of the LiDAR data to the application of the methods, for validation purposes. Both simulated and experimental results are provided for the performance evaluation of the perception algorithms. Building on these results, a nonlinear control strategy is designed with the objective of providing an accurate trajectory tracking control relative to the structure, with guaranteed asymptotic stability.
From evidence to realities: psychosocial intervention provision in Australian routine community mental health practice

ABSTRACT Background This study explored care co-ordinator reports about provision of six evidence-based psychosocial interventions (PSIs) for people living with psychosis, to better understand decision-making about provision. Methods Conducted as an adjunct to the Second Australian National Survey of Psychosis in one catchment area, care co-ordinators completed a structured interview about evidence-based PSIs provided to 33 consumers as part of their community mental health care. Descriptive analyses were conducted. Results Care co-ordinators reported most consumers were provided at least one of the PSIs during the previous year; all were provided at least one non-evidence-based, typically briefer or simpler, alternative. Relapse Prevention Planning using early warning signs was reported as most provided; Family Psychoeducation was the least provided but rated as most helpful. The primary reason for non-provision of PSIs was that they had not been offered, with lack of relevance the most cited explanation. Conclusions PSIs may be more commonly provided than previously reported, if non-evidence-based alternatives are also considered: reasons for not offering evidence-based PSIs require further study. Meaningful guidelines are needed about when and how to offer PSIs in collaborative practice, including briefer or simpler interventions when preferred over more complex interventions.
Architectural Design of a Fast Search Algorithm and Implementation to Intra-Mode Decision Block of Still Image Coding

As a requirement of many modern image compression standards faced today, a computational complexity is observed due to the best mode selection in the intra-prediction stage. This computational complexity is tried to be reduced by various techniques without affecting the performance criteria of the image. In this study, a fast search algorithm, which simplifies the mode selection process of the intra-prediction algorithm and provides calculation with less number of modes is proposed. The hardware architecture of this proposed algorithm is implemented for realization. There are two main sections of the intra-prediction algorithm in image compression, namely the image prediction process and the mode selection process. In this study, main objective is to reduce the process time of the mode selection and the simplification of the hardware design. Sum of absolute difference (SAD) is a frequently used criterion to simplify hardware design. The algorithm searches for the most suitable mode in a single step, where the decision is based on the SAD criterion preferred for the simplicity. The proposed algorithm and related hardware architecture is tested by using various experiments. The number of the modes calculated is reduced effectively, while the process is kept within the acceptable limits in terms of peak signal to noise ratio (PSNR) and compression rate (CR) performance criteria. Therefore, the number of clock cycles observed is considerably reduced. The designed architecture is synthesized for the field programmable gate arrays (FPGA) board and the obtained results are given. In addition, these results are compared with the HM reference software where the corresponding results are in accordance with the reference software.
Analysis of barriers and facilitators to early hearing detection and intervention in KwaZulu-Natal, South Africa

Background There is slow progress in early hearing detection and intervention (EHDI) services within South Africa. Audiologists are EHDI gatekeepers and can provide valuable insights into the barriers and facilitators that can progressively move EHDI towards best practice in South Africa. Objectives The study aimed to determine the barriers and facilitators to EHDI in KwaZulu-Natal as reported by audiologists/speech therapists and audiologists (A/STAs). Method A descriptive qualitative approach was used. Telephonic interviews were conducted with 12 A/STAs working in public and private healthcare facilities, using the strengths, weaknesses, opportunities, threats (SWOT) conceptual framework. Data was analysed using thematic analysis in conjunction with NVivo software. Results One of the main barriers perceived by A/STAs, affecting EHDI was the lack of resources in healthcare facilities. The participants indicated that although there was a guideline in place to guide practice, it may be more suited to an urban area versus a rural area. Poor knowledge and awareness of EHDI was also identified as a barrier. Information provided from A/STAs at grassroots level, in the various provinces, may benefit in developing a more contextually relevant and practical guideline. Facilitators included; development of task teams specifically for EHDI programmes, creation of improved communication networks for collaboration and communication, training of healthcare professionals and improving data management systems. Conclusion Strategies such as an increase in resources, further education and training, development of contextually relevant, culturally, and linguistically diverse practices and protocols need to be in place to improve EHDI implementation. Further research, clinical implications and limitations are provided emanating from the study.
Influence of Air Distribution in the Furnace on Combustion Characteristics of the Boiler

To know the effect of the different ratio of the air on the process of combustion,the fluid flowing,coal combustion are modeled numerically and proceeded an optimal combustion control test in the case of different operational conditions about the Tangential Fired Fumace.The results indicate that: for a fixed load,if the ratios of the primary air are decreased appropriately,it can make the flame full of the hearth and shorten the length of the flame;And the appropriate secondary air distribution can make the bad coal burning steadily;Stop providing the tertiary air,the temperature became higher in the hearth,the coal have a full combustion,and the boiler efficiency improved.
Observations Indicate That Clouds Amplify Mechanisms of Southern Ocean Heat Uptake

The Southern Ocean has absorbed most of the excess heat associated with anthropogenic greenhouse gas emissions. Since Southern Ocean observations are sparse in certain regions and seasons, much of our knowledge of ocean heat uptake is based on climate model simulations. However, climate models still inadequately represent some properties of Southern Ocean clouds, and they have not identified the mechanisms by which clouds may affect Southern Ocean heat uptake (SOHU). Here, we use the ERA5 and JRA‐55 reanalyses to assess the influence of clouds and other atmospheric processes on SOHU from 1979 to 2020. We find that years with the highest SOHU between 45° and 65°S are dominated by ocean heat uptake anomalies during winter and spring, but not during summer or fall. Winter and spring cloud cover are up to 7% higher when SOHU is up to 5.5 W/m2 higher than the climatological seasonal mean, with the largest increases in the South Pacific Ocean. Clouds also contain more liquid water. These changes in cloud properties increase downwelling longwave radiation, amplifying ocean heat uptake. Cloud changes are also concomitant with a more stable lower atmosphere, which suppresses turbulent heat fluxes out of the surface. Overall, we find that SOHU is likely not mediated by enhanced surface shortwave absorption over the observational time period. A better understanding of how atmospheric processes impact ocean heat uptake may help improve our understanding of ocean heat uptake mechanisms in the current generation of climate models.
Digital transformation, green innovation and the Solow productivity paradox

To provide evidence at the micro level for cracking the Solow productivity paradox, this paper deeply studies the impact of enterprise digital transformation on green innovation. In terms of theoretical research, three potential mechanisms are excavated for the first time; considering empirical research, a series of strict causal effect identification strategies are carried out. The results show that enterprise digital transformation can significantly promote green innovation, and it passes a series of robustness tests and endogenous tests. According to the theoretical and empirical results, the policy suggestions mainly include five points: helping enterprises to accelerate digital transformation; strengthening the green innovation ability of enterprises; reducing internal and external costs and promoting the professional division of labor; piloting the digital transformation policy; enhancing corporate social responsibility. It provides a reference of experience and a path for other countries to follow in implementing a digital transformation strategy and green sustainable development strategy.
Calcium Homeostasis and Bone Metabolism in Goats Fed a Low Protein Diet

The objective of this study was to investigate the effects of low-protein diets on blood calcium (Ca) level, bone metabolism, and the correlation between bone metabolism and blood calcium in goats. Twenty-four female Xiangdong black goats with similar body weight (19.55 ± 3.55 kg) and age (8.0 ± 0.3 months) were selected and allocated into two groups: control group (CON, 10.77% protein content) and low-protein group (LP, 5.52% protein content). Blood samples were collected on days 1, 4, 7, 16 and 36 before morning feeding to determine the concentration of calcium (Ca), parathyroid hormone (PTH), bone gla protein (BGP), C-terminal telopeptide of type 1 collagen (CTX-1), bone alkaline phosphatase (BALP), and 1, 25-dihydroxyvitamin D3 [1,25(OH)2D3]. Liver samples were collected to determine the expression of bone metabolism-related genes. There was no difference observed between LP and CON in concentration of plasma Ca or any of bone metabolism markers (P > 0.05). In the liver, the mRNA expression of bone gamma carboxyglutamate protein (BGLAP), alkaline phosphatase (ALPL), and mothers against decapentaplegic homolog-1 (SMAD1) were increased (P < 0.05) in LP as compared with CON. The correlation analysis of Ca and bone metabolism markers showed no significant correlation between Ca and bone metabolism. These results suggest that the blood Ca concentration in mature goats may keep at a stable level through nitrogen cycling when the providing protein is not enough.
Identification of Wheat Leaf Diseases Based on SVM Method

In order to identify wheat blade diseases accurately and provide scientific basis for wheat diseases diagnosis,an identification method of wheat blade disease based on SVM was presented.Three wheat blade diseases,such as Powdery mildew,Strip rust disease and Blade rust disease,were recognized accurately via this method.First,image de-noising and spot segmentation were achieved by using median filter method and K-means clustering algorithm.Then,two kinds of disease spot features were extracted,including shape features and texture features.Finally,disease blades were classified and identified using SVM algorithm.Experiment results showed that the presented method which took advantage of the disease spot features could identify wheat blade diseases efficiently,and the comprehensive identification rate of shape features based method reached 99.33%,which showed that it was very effective and practicable for identifying wheat blade diseases by using SVM algorithm.The presented method also provides reference for intelligent identification of crop diseases in the future research.
The of role economic growth in modulating mobile connectivity dynamics for financial inclusion in developing countries

This study establishes economic growth needed for supply-side mobile money drivers in developing countries to be positively related to mobile money innovations in the perspectives of mobile money accounts, the mobile phone used to send money, and the mobile phone used to receive money. The empirical evidence is based on Tobit regressions. For the negative net relationships that are computed, minimum economic growth thresholds are established above which the net negative relationships become net positive relationships. The following minimum economic growth rates are required for nexuses between supply-side mobile money drivers and mobile money innovations to be positive: (i) 6.109% (6.193%) of GDP growth for mobile connectivity performance to be positively associated with the mobile phone used to send (receive) money and (ii) 4.590 % (4.259%) of GDP growth for mobile connectivity coverage to be positively associated with the mobile phone used to send (receive) money.
6S RNA-dependent antibiotic susceptibility

Bacterial small RNAs (sRNAs) contribute to a variety of regulatory mechanisms that modulate wide ranging pathways, including metabolism, virulence, and antibiotic resistance. We investigated the involvement of sRNAs in rifampicin resistance in the opportunistic pathogen Staphylococcus aureus. Using a competition assay with an sRNA mutant library, we identified 6S RNA as being required for protection against low concentrations of rifampicin, an RNA polymerase (RNAP) inhibitor. This effect applied to rifabutin and fidaxomicin, two other RNAP-targeting antibiotics. 6S RNA is highly conserved in bacteria and its absence in two other major pathogens, Salmonella enterica and Clostridioides difficile, also impaired susceptibility to RNAP inhibitors. In S. aureus, 6S RNA is produced from an autonomous gene and accumulates in stationary phase. In contrast to what was reported in Escherichia coli, S. aureus 6S RNA does not appear to play a critical role in the transition from exponential to stationary phase, but affects σB-regulated expression in prolonged stationary phase. Nevertheless, its protective effect against rifampicin is independent of alternative sigma factor σB activity. Our results suggest that 6S RNA helps maintain RNAP-σA integrity in S. aureus, which could in turn help bacteria withstand low concentrations of RNAP inhibitors.
A Blockchain-Enabled Demand Management and Control Framework Driven by Deep Reinforcement Learning

The rapid development of Internet-of-Things in smart grid has enabled millions of grid-connected distributed controllable resources (DCR; e.g., electric vehicles, controllable loads) to provide service to the grid, such as frequency regulation and demand response. The integration of these DCRs may become a large virtual power plant network with various characteristics. This poses great challenges from both control and management perspectives, e.g., computation/communication burden, optimization complexity, scalability limitation, prosumer privacy, etc. In this article, we propose an effective autonomous incentive-based DCR control and management framework to integrate a large amount of DCRs to provide grid services, which simultaneously provides accurate active power adjustment to the grid, optimizes DCR allocations, and maximizes the profits for all prosumers and system operators. A model-free deep deterministic policy gradient-based method is designed to find the optimal incentives in a continuous action space to encourage prosumers to adjust their power consumptions. The method is implemented in a consortium open-source blockchain platform, Hyperledger Fabric, which facilitates controls and transaction management. To demonstrate the effectiveness of the framework, extensive experimental studies are conducted using real-world data.
Effect of Different Solvent on Phytochemical Content, Tyrosinase Inhibition and Antioxidant Activities of Campolay (Pouteria campechiana kunth. [Baehni.])

BACKGROUND: Pouteria campechiana leaves are reported to have phenol and flavonoid compounds. Phenolic and flavonoid compounds can act as tyrosinase inhibitor and antioxidant. AIM: The purpose of this study was to compare phytochemical content, tyrosinase inhibition, antioxidant activities, and determine of marker compound from P. campechiana leaves extract with different polarities solvent. METHODS: In addition, the content of marker compound from P. campechaina leaves extract was determined by HPLC. RESULTS: The highest total phenolic content (TPC) 7.83 GAE/100 g extract, IC50 of tyrosinase 171.512 ± 1.352 and IC50 of DPPH 0.968 ± 0.008 was given by ethanolic extract (DE). Meanwhile ethyl acetate extract (DEA) had the highest of total flavonoid content 2.544 ± 0.554 QEA/100 g extract. The TPC correlated with tyrosinase inhibitory activity and antioxidant activity. CONCLUSION: Quercitrin was as marker compound from P. campechiana leaves extract, and quercitrin content in the DEA of P. campechiana leaves was 3.539%, while in the DE was 0.153%.
Plant recognition method based on leaf images.

This paper proposes a plant automatic recognition method based on the leaf images,to be used in the computer-based automatic recognition system of the plant taxonomy.The color image is first transformed into a grey-level and a binary image in pre-processing including brightness correction,median filter and threshold segmentation.Six relative shape parameters and 5 texture parameters are then calculated,respectively,from the binary image and the grey-level image.The 6 shape parameters are the eccentricity,roundness,roundness index,direction angle,width and length ratio of the smallest rectangle that can cover the leaf,and short and long axes ratio of the best ellipse that can cover the leaf;and the five texture parameters are the second moment,contrast,correlation,entropy,moment deficit.Finally,an automatic recognition classifier based on RBF neural network is designed to determine which type of plant the leaf is from with the image of the leaf being used as a sample.Then,60 images of leaf from 3 plants are used as samples to test the performance of the automatic recognition classifier.The average correct recognition rate reaches 70.83% when only the 6 shape parameters are used as the input data of the classifier,and it reaches 83.33% when both shape parameters and texture parameters are used as the input data of the classifier.The results show that the texture features can improve the average correct rate,and that the plant automatic recognition based on the leaf images is feasible.
Beyond the Limits of Predictability in Human Mobility Prediction: Context-Transition Predictability

Urban human mobility prediction is forecasting how people move in cities. It is crucial for many smart city applications including route optimization, preparing for dramatic shifts in modes of transportation, or mitigating the epidemic spread of viruses such as COVID-19. Previous research propose the maximum predictability to derive the theoretical limits of accuracy that any predictive algorithm could achieve on predicting urban human mobility. However, existing maximum predictability only considers the sequential patterns of human movements and neglects the contextual information such as the time or the types of places that people visit, which plays an important role in predicting one's next location. In this paper, we propose new theoretical limits of predictability, namely Context-Transition Predictability, which not only captures the sequential patterns of human mobility, but also considers the contextual information of human behavior. We compare our Context-Transition Predictability with other kinds of predictability and find that it is larger than these existing ones. We also show that our proposed Context-Transition Predictability provides us a better guidance on which predictive algorithm to be used for forecasting the next location when considering the contextual information. Source code is at https://github.com/zcfinal/ContextTransitionPredictability.
AIMSim: An accessible cheminformatics platform for similarity operations on chemicals datasets

The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery applications to assess how new molecules differ from existing ones. Most tools target advanced users and lack general implementations accessible to the larger community. In this work, we introduce Artificial Intelligence Molecular Similarity (AIMSim), an accessible cheminformatics platform for performing similarity operations on collections of molecules (molecular datasets). AIMSim provides a unified platform to perform similarity-based tasks on molecular datasets, such as diversity quantification, outlier and novelty analysis, clustering, and inter-molecular comparisons. AIMSim implements all major binary similarity metrics and molecular fingerprints and is provided as a Python package that includes support for command-line use as well as a fully functional Graphical User Interface for code-free utilization.
Optimum Quality Control Algorithm for Versatile Video Coding

We propose a quality control method that replaces rate control in video coding and achieves quality control with the minimum bit rate. First, based on the rate–distortion model, we establish a Karush–Kuhn–Tucker equation considering the convergent properties of variational inequalities to solve the continuous convex problem. Second, combined with the rate–quality (R-Q) model, the proposed algorithm is implemented under versatile video coding (VVC) to obtain the optimal closed-form solution for the quantization parameters of a coding unit through the Lagrange multiplier method. Experiments reveal that our algorithm can achieve stable video coding quality while guaranteeing coding performance, achieving high quality control accuracy with constant quality.