Miracle-dz/UniFAR-FTUs huggingface dataset:
Historical air photo missions in the Maritimes during the early 1920s: coverage, thematic scope, and utility 100 years later

The historical and technological developments of powered flight and aerial photography have early connections in the Maritimes. Following the Great War (1914-18), a series of pioneering survey missions were initiated by the Canada Air Board in the civilian domain. From a science perspective, the air photos offer a unique opportunity for the detection of environmental change at an unusual centennial time scale. The missions of the early 1920s initially relied on military surplus seaplanes and innovative camera equipment that yielded several thousand high-resolution vertical air photos. This paper is focussed on the scope and outcome of the first experiments carried out over Nova Scotia and New Brunswick between 1921 and 1925, prior to more systematic use for topographic mapping during the remainder of that decade. The research is based on archival records and partial reconstruction of the digitized air photos into image mosaics. Photo interpretation and comparison with recent high-resolution satellite imagery offer insights concerning land use and land cover changes, coastal dynamics, and transformation of urban, rural and industrial landscapes. Experience todate with these early air photos and mosaics of the Maritimes holds promise for examining similar aerial survey missions in other parts of Canada. Historical air photo missions in the Maritimes during the early 1920s:
Normal contact stiffness model considering 3D surface topography and actual contact status

Abstract. A normal contact stiffness model considering 3D topography and elastic–plastic contact of rough surfaces is presented in this paper. The asperities are generated from the measured surfaces using the watershed segmentation and a modified nine-point rectangle. The topography parameters, including the asperity locations, heights, and radii of the summit, are obtained. Asperity shoulder–shoulder contact is considered. The relationship of the contact parameters, such as the contact force, the deformation, and the mean separation of two surfaces, is modelled in the three different contact regimes, namely elastic, elastic–plastic and fully plastic. The asperity contact state is determined, and if the contact occurs, the stiffness of the single asperity pair is calculated and summed as the total normal stiffness of two contact surfaces. The developed model is validated using experimental tests conducted on two types of specimens and is compared with published theoretical models.
Research on Wind Environment Simulation of Commercial District Based on Phoenics—Taking Shanghai Central Building Group as an Example

As one of the central cities of my country's economic construction integration, Shanghai has continuously emerged with high-rise buildings under the trend of a threedimensional urban process. At the same time, the urban environment has become a problem that cannot be ignored, so it is necessary to conduct in-depth research on the wind environment of the building complex. Aiming at the wind environment problems in the city, this paper takes Shanghai’s central business district as an example, uses the software Phoenics to simulate the wind environment of the downtown building group, analyzes the types and functions of the business district building group, and aims at the existing wind environment propose solutions to the problems and provide reference theoretical basis and optimization basis for the formulation of relevant planning and design standards for the central area.
A Data Access Control Method Based on Blockchain

In the digital era, a large amount of data is generated every day and recorded on the storage system. Access control is an important mechanism for ensuring data privacy and security. Data accessing control model of traditional storage system is centralized, which has threatened the information security of users. As blockchain is emerging technology which is widely used in the area of data privacy preserving, in this paper we proposed a decentralized access control model via blockchain based smart contracts implementation. Once satisfying the conditions set by users, visitors could apply to the blockchain for authentication, then obtain permission to access user data and operate the data, finally achieving secure access control of user data. In our experiment, we build the blockchain system based on Hyperledger Fabric project and evaluate both the efficiency of writing and the verification of access permission. The result shows our model’s usability and efficiency.
Development and application of substation intelligent inspection robot supporting deep learning accelerating

Substation inspection robots usually have low computation capability to run deep learning models. In this paper, a substation intelligent inspection robot that can support real time deep learning models is developed. The Nvidia Jetson TX2 module is the accelerating hardware module of the robot, and TensorRT is the software framework for deep learning model inference accelerating. The robot can satisfy the computational requirement of deep learning based applications, and operate in very low energy consumption. Test results on a fault diagnostic task based on object detection and instance segmentation show that with deep learning accelerating, not only high detection accuracy is achieved, but also inference time is short enough for real time fault diagnostic application.
Electronic Participation Technology Adoption Model in Indonesia: The Role of Gamification Impact

This study aims to propose a conceptual model in the context of e-participation technology adoption. This research develops the UTAUT base-line model with several constructs, namely Insecurity and Gamification. The impact of gamification to increase user participation is not widely known, especially regarding the acceptance of e-participation. The method used is a literature study related to several best practice models such as UTAU, TRI and gamification constructs. The results showed a total of 7 constructs and 6 hypotheses proposed in this study. All these constructs are performance performance expectancy, effort expectancy, social influence, facilitating conditions, insecurity, gamification impact and behavioral intention to use. The implication of this research is that the lack of public participation in digital platforms can be increased through redesign of e-participation by applying the gamification approach.
Procedures for Verification of Instruments for Thermal Environment Measurement

Accuracy and verification of instruments for thermal environment measurement are important to determine the reliability of measured parameters. The purpose of this study is to propose simple procedures for consistency check on four groups of instruments used in thermal environment measurement: indoor air temperature, outdoor air temperature, relative humidity, and air velocity. For indoor air temperature measurement, the existing instruments were checked with the new thermocouple connected to a data logger in an air-conditioned room. The measurement was conducted under different set point temperatures. For relative humidity measurement, the readings of the measurement instruments were measured in a naturally ventilated room and compared with those of the digital ventilation psychrometer. For air velocity measurement, the instruments were set in a mini handmade wind tunnel. The air velocity readings obtained from the instruments were compared to verify the result consistency. Finally, for outdoor air temperature measurement, all instruments were placed in a fan aspirated solar shield which was set outdoors. For each group of the instruments, the obtained readings were evaluated based on the coefficient of determination and standard deviation from the mean values. The results have shown that all air temperature and relative humidity instruments have strong correlation owing to the high coefficient of determination obtained. The mean air velocity obtained from all measurement instruments ranged from 3.24 to 3.40 m/s with the standard deviation of 0.07 to 0.1. Furthermore, the mean outdoor air temperature ranged from 25.3 to 25.6°C with the standard deviation of 1.04 to 1.08. The simple procedures carried out in this work successfully verified the consistency of all measurement instruments, and this is essential before the instruments are used in actual field measurements.
Experimental Study of Forced Convection Heat Transfer through Porous Media inside a Rectangular Duct of Fully Developed Region

This prospective study was designed to experimentally investigate the effects of inserting copper mesh in fully developed turbulent flow on the flow and heat transfer characteristics. Experimental work includes designing and manufacturing the test section of rectangular duct with dimensions of (30*3*40 cm) with aspect ratio and hydraulic diameter of 10 and 5.45 mm respectively. A constant heat flux ranged (1.5*102 – 1.8*102 W/m2) was applied to the lower surface of duct with Reynolds number range (3.3*104 – 4.8*104). The porosity rang of (0.98 – 0.99). Also, the effects of porous height ratio (full and partial filling) were considered and considered air as working fluid. The results indicate that inserting mesh wire leads to enhance heat transfer coefficient and increase pressure drop. Taken together, these results show that the obtained values of performance enhancement criteria (PEC) ranged (1.01 -1.98 ), the enhancement in heat transfer coefficient is 315% duo to wire mesh insert. Also, from obtained results empirical correlation for friction factor and Nusselt number were suggested.
Simulations of microturbulence in magnetised plasmas using a delta-f gyrokinetic approach with an evolving background Maxwellian

The gyrokinetic delta-f particle-in-cell (PIC) approach is known to be successful for simulating turbulence in the core of magnetic fusion plasmas, where fluctuations are relatively small and therefore the unperturbed particle distribution function, usually represented by a stationary Maxwellian f 0, remains a good choice of a control variate for reducing statistical sampling noise. However, towards the plasma edge, characterized by low density and temperature and strong gradients, relative deviation amplitudes typically become large, so that the essential assumption of |δf/f 0| << 1 underlying the delta-f PIC approach will not be valid, where δf is the fluctuating part of distribution. This motivates the study of the limits of the delta-f approach in a simplified system mimicking the plasma edge. To this end, simulations are run using GK-engine, which is a delta-f PIC code that solves the nonlinear gyrokinetic equation in a sheared slab geometry, using B-spline finite elements to represent the self-consistent electrostatic field. Initial radial density and ion temperature profiles exhibiting high logarithmic gradients representing plasma edge conditions are used. In order to avoid practical problems of particles exiting the simulation domain as the ion temperature profile relaxes, all profiles are mirrored at domain-centre and periodic boundary conditions are imposed. The validity of the delta-f approach is measured by statistical noise estimates, while monitoring relative deviation levels of temperature via the kinetic energy. In particular, the effect of background profile gradients on these measures is investigated. As a first step towards reducing the amplitude of the deviation δf, an adaptive Maxwellian f 0 is implemented, whose time dependent temperature profiles are obtained by locally relaxing kinetic energy accumulating in δf into f 0.
Multi-timescale Active Distribution Network Scheduling Considering Demand Response and User Comprehensive Satisfaction

A large number of distributed energy resources (DERs) integrate into the distribution network, which changes the power flow, increases the power fluctuations, and complicates the scheduling of the distribution network. To cope with that, a multitimescale scheduling method, which considers the demand response as well as user satisfaction, is proposed in this paper. First, in the day-ahead stage, both the generation-side and demand-side are combined to minimize the operating costs and reduce the impact of DERs. Second, in the real-time stage, the model predictive control method is introduced, smoothing the power fluctuations and maximizing the consumed renewable energy. Finally, the user comprehensive satisfaction is considered, ensuring the users’ benefit and improving the flexibility of users to participate in scheduling while shifting electricity demands. By optimizing the generation-side and demand-side on both day-ahead and real-time timescales, the proposed method can improve the operation status for the distribution network effectively while ensuring the interests of users. Simulation on an improved IEEE-33 bus distribution system verifies the effectiveness of the proposed method.
Stevia rebaudiana (Bertoni) as a Multifunctional and Sustainable Crop for the Mediterranean Climate

Stevia rebaudiana (Bertoni) is a promising medicinal and aromatic plant for Mediterranean agroecosystems given its positive agronomic attributes and interesting quality features. It has both food and pharmaceutical applications, since its leaves contain sweet-tasting steviol glycosides (SVglys) and bioactive compounds, such as phenolics, flavonoids, and vitamins. We evaluated the agronomic and qualitative performances of nine stevia genotypes cultivated, in open field conditions, for two consecutive years under the Mediterranean climate of central Italy. Growth, biomass production, and accumulation of bioactive compounds (SVglys, polyphenols, and their related antioxidant activities) were evaluated, considering the effect of harvest time and crop age (first and second year of cultivation). The results showed high variability among genotypes in terms of both morpho-productive and phytochemical characteristics. In general, greater leaf dry yields, polyphenol accumulation, and antioxidant activities were found in the second year of cultivation, harvesting the plants in full vegetative growth. On the other hand, total SVglys leaf content reached the highest values in the first year when plants were at the beginning of the reproductive phase. On the other hand, although the SVglys profile (Rubusoside, Dulcoside A, Stevioside, Rebaudioside A, C, D, E, and M) remained stable over harvest times, it differed significantly depending on the crop age and genotype. Our findings provide useful information on the influence of crop age and harvest time in defining quanti-qualitative traits in stevia, with PL, SL, BR5, and SW30 being the best performing genotypes and thus suitable for breeding programs. Our study highlighted that stevia, in the tested environment, represents a promising semi-perennial crop which offers new solutions in terms of cropping system diversification and marketing opportunities.
Non-Invasive Testing of Physical Systems Using Topological Sensitivity

A review of available results on non-destructive testing of physical systems, using the concept of topological sensitivity, is presented. This mathematical tool estimates the sensitivity of a set of measurements in some given sensors, distributed along the system, to defects/flaws that produce a degradation of the system. Such degradation manifests itself on the properties of the system. The good performance of this general purpose post-processing method is reviewed and illustrated in some applications involving non-destructive testing. These applications include structural health monitoring, considering both elastodynamic ultrasonic guided Lamb waves and active infrared thermography. Related methods can also be used in other fields, such as diagnosis/prognosis of engineering devices, which is also considered.
Multi-Parameter Neural Network for Altimeter Tropical Cyclone Wind Speed Estimation

The ability of satellite altimeter to estimate wind speed in tropical cyclone condition has been investigated. In the extreme condition with higher spatio-temporal variation, the ocean-atmosphere interaction is very complex and makes the existing algorithm become an ill-posed solution. In such condition, the developed algorithm from single frequency backscatter and significant waves height were insufficient. Besides, wind speed estimates become saturated at high regimes and the reflected backscatter was contaminated by rain. Therefore, other simultaneously observed parameters are needed to comprehensively account for this condition and is expected to improve the accuracy of wind speed retrieval. Aside from altimeter instrument, the microwave radiometer onboard Jason-2 concurrently records the brightness temperature and the rain information. To accommodate related multiple parameters for wind speed derivation, the neural network approach is proposed. Its unique advantage is relationship among multi-parameters can be easily established without prior knowledge on their physical attributes. Therefore, this study intended to determine the multi-parameter neural network (MPNN) model in estimating altimeter wind speed during the tropical cyclone condition. The results proved that the MPNN technique has potential in reducing the root mean square error by 30% in comparison between tropical cyclone wind speed estimate by the existing algorithm.
Impact of vacancies on the thermal conductivity of graphene nanoribbons: A molecular dynamics simulation study

Equilibrium molecular dynamics simulation using 2nd generation Reactive Bond Order interatomic potential has been performed to model the thermal transport of nanometer sized zigzag defected graphene nanoribbons (GNRs) containing several types of vacancies. We have investigated the thermal conductivity of defected GNRs as a function of vacancy concentration within a range of 0.5% to 5% and temperature ranging from 300K to 600K, along with a comparative analysis of those for pristine GNRs. We find that, a vacancy concentration of 0.5% leads to over 90% reduction in the thermal conductivity of GNRs. At low defect concentration, the decay rate is faster but ceases gradually at higher defect concentration. With the increasing temperature, thermal conductivity of defected GNRs decreases but shows less variation in comparison with that of pristine GNRs at higher temperatures. Such comprehensive study on several vacancy type defects in GNRs can provide further insight to tune up the thermal transport characterist.
Continuous nanoparticles production through a combination of a micro electro mechanical system and an electromagnetic resonator cavity

ABSTRACT In the present work, a reliable, rapid, and controllable method is developed for the continuous generation of pharmaceutical curcumin and loratadine nanoparticles (NPs). Micro droplets of curcumin and loratadine were generated by atomization of their corresponding solutions by a combination of a micro electro mechanical system and an electromagnetic resonator cavity. After evaporation of the solvent of micro droplets by electromagnetic waves, the NPs were collected by a polytetrafluoroethylene (PTFE) filter. NPs were characterized by scanning electron microscopy. Results showed that this method can be an effective way to produce amorphous NPs with narrow particle size distribution, where particle size can be easily controlled by solution concentration. Particles size varies from 152 to 446 nm and from 116 to 719 nm for loratadine and curcumin, respectively.
Over expressions of neutrophil gelatinase-associated lipocalin and kidney injury molecule-1 in human uterine cervical neoplasms enhance tumor invasion

Pathology Laboratory,SBU İzmir Tepecik Education and ResearchHospital, 35020Konak/İzmir, Turkey PathologyDepartment, İzmir DemocracyUniversity, 35140Karabağlar/İzmir, Turkey PathologyDepartment, AkhisarMustafa Kirazoglu StateHospital,Manisa, 45215 Akhisar/Manisa, Turkey PathologyDepartment, Edirne TrakyaUniversity, 22030 Edirne, Turkey Gynecologic OncologyDepartment, SBU İzmir Tepecik Education and ResearchHospital, 35020Konak/İzmir, Turkey
Control method of power grid topology structure based on reinforcement learning

With the rapid development of renewable energy and power electronics technology, uncertainty, complexity and data accumulation in power system continue to increase. Traditional methods often encounter bottlenecks in solving operational optimization decision-making and control problems. The development of artificial intelligence technology provides new methods and means for solving the problem of intelligent control of power grid topology. The deep reinforcement learning (DRL) method is used to learn from the historical experience data of power grid topology control that can improve the control and decision-making and methods of system operating performance, and solve the huge variables in traditional models. Through the method in this paper, the DRL method can effectively improve the real-time optimization and control ability of the grid topology.
Assessing CO2 Emissions from Passenger Transport with the Mixed-Use Development Model in Shenzhen International Low-Carbon City

Assessing transport CO2 emissions is important in the development of low-carbon strategies, but studies based on mixed land use are rare. This study assessed CO2 emissions from passenger transport in traffic analysis zones (TAZs) at the community level, based on a combination of the mixed-use development model and the vehicle emission calculation model. Based on mixed land use and transport accessibility, the mixed-use development model was adopted to estimate travel demand, including travel modes and distances. As a leading low-carbon city project of international cooperation in China, Shenzhen International Low-Carbon City Core Area was chosen as a case study. The results clearly illustrate travel demand and CO2 emissions of different travel modes between communities and show that car trips account for the vast majority of emissions in all types of travel modes in each community. Spatial emission differences are prominently associated with inadequately mixed land use layouts and unbalanced transport accessibility. The findings demonstrate the significance of the mixed land use and associated job-housing balance in reducing passenger CO2 emissions from passenger transport, especially in per capita emissions. Policy implications are given based on the results to facilitate sophisticated transport emission control at a finer spatial scale. This new framework can be used for assessing the impacts of urban planning on transport emissions to promote sustainable urbanization in developing countries.
Brain tumor detection from MRI images using deep learning techniques

Brain tumor is the growth of abnormal cells in brain some of which may leads to cancer. The usual method to detect brain tumor is Magnetic Resonance Imaging(MRI) scans. From the MRI images information about the abnormal tissue growth in the brain is identified. In various research papers, the detection of brain tumor is done by applying Machine Learning and Deep Learning algorithms. When these algorithms are applied on the MRI images the prediction of brain tumor is done very fast and a higher accuracy helps in providing the treatment to the patients. These prediction also helps the radiologist in making quick decisions. In the proposed work, a self defined Artificial Neural Network (ANN) and Convolution Neural Network (CNN) is applied in detecting the presence of brain tumor and their performance is analyzed.