Miracle-dz/UniFAR-FTUs huggingface dataset:
Cluster Approach For Territorial Development Management

The paper is dedicated to the application of cluster approach to manage territory development using artificial intelligence for making rational managerial decisions. The aim set in the paper consists in the necessity to form different approaches to manage regions depending on the region social-economic indexes. 14 parameters were chosen for this, based on which the regions were clustered into 3 groups. For regions from different clusters different approaches for managing them can be applied to reveal weak indexes of the region and make the decision on changing their development strategy and improving their social-economic conditions. To reach the foregoing aim the authors applied the clusterization algorithm: self-organizing maps using analytical platform “Deductor”. The research results are significant and important for the economy as a whole. The Deductor analytical platform has provided complete solutions related to the processing and analysis of structured data. The field of application of the research results is associated with the subsequent formation of proposals and recommendations for the development of organizational, legal, methodological, economic and other measures to manage the development of territories.
Facial Expression Recognition Method Combined with Attention Mechanism

Aiming at the slow speed and low accuracy of traditional facial expression recognition, a new method combining the attention mechanism is proposed. Firstly, group convolution is used to reduce network parameters. The channels of traditional convolution are grouped to cut off redundant connections so that the number of parameters decreases significantly. Secondly, the ERFNet network model was improved by combining the asymmetric residual module and the weak bottleneck module to improve the running speed and reduce the loss of accuracy. Finally, the attention mechanism was added into the feature extraction network to improve the recognition precision. The experiment shows that compared with traditional face recognition methods, the proposed method can improve the recognition precision and recall significantly; in CK+, Jaffe, and Fer2013 datasets, the recognition precision can reach 88.81%, 82.16%, and 79.33%, respectively.
Energy and economic analysis of photovoltaic, concentrating photovoltaic, and combined concentrating photovoltaic/thermal‐organic Rankine cycle power plants in Iran

In this study, energy and economic analyses of three small‐scale solar power plants were conducted in various parts of Iran. The power plants were polycrystalline photovoltaic (PV), concentrating photovoltaic (CPV), and combined concentrating photovoltaic/thermal (CPV/T) and Rankine organic cycle (ORC). Electricity productions for the PV and CPV power plants were initially obtained. The electricity generation from the combined CPV/T and ORC power plant was then calculated and compared with the other two plants studied. In addition, an economic analysis of all the plants studied was also conducted. All calculations have been performed using in‐house computer code written in MATLAB software. Results showed that the CPV and CPV/T and ORC plants are most suitable for those regions of Iran where the global annual horizontal solar energy is more than 1900 and 2000 kWh/m2, respectively. In these regions, the CPV and CPV/T and ORC plants generate up to 200% of the electrical energy generated by the PV plant. Based on the current feed‐in tariff for photovoltaic systems in Iran, the economic analysis suggests that when the land costs are not a major concern in all the plants under consideration, the CPV and CPV/T and ORC plants are mainly profitable in the regions where the annual horizontal solar energy is more than 1900 kWh/m2.
On allowing for transient variation in end‐member δ13C values in partitioning soil C fluxes from net ecosystem respiration

The use of stable isotope analysis to resolve ecosystem respiration into its plant and soil components rests on how well the end‐member isotope signatures (δ13C) are characterised. In general, it is assumed that end‐member values are constant over time. However, there are necessarily diurnal and other transient variations in end‐members with environmental conditions. We analyse diurnal and seasonal patterns of ecosystem respiration and its δ13C in a C4 grass growing in a C3 soil using fixed and diurnally varying plant and soil δ13C end‐members. We measure the end‐members independently, and we assess the effects of expected variation in values. We show that variation in end‐members within realistic ranges, particularly diurnal changes in the plant end‐member, can cause partitioning errors of 40% during periods of high plant growth. The effect depends on how close the end‐member is to the measured net respiration δ13C, that is, the proportion of the respiration due to that end‐member. We show light‐driven variation in plant end‐members can cause substantial distortion of partitioned soil organic matter (SOM) flux patterns on a diurnal scale and cause underestimation of daily to annual SOM turnover of approximately 25%. We conclude that, while it is not practicable to independently measure the full temporal variation in end‐member values over a growing season, this error may be adjusted for by using a diurnally varying δ13Cplant.
Design and implement a smart traffic light controlled by internet of things

The rise of the population produces an increase in the number of vehicles on the road, which creates heavy traffic in the roads and that causes many issues for the citizens and traffic cops an extra two emergency instances so it’s necessary with developing technology to solve this problem. in this research, we used the Arduino UNO microcontroller board to build a new smart traffic light controller (STLC). Signal lights produce traffic congestion, and the system makes every attempt to alleviate it. In this paper, we designed a smart traffic control system by using Arduino to solve the problem of congestion at the intersection of the Dor al Moalemen in Wasit city , working to prevent traffic jam and reduce time, Using Arduino mega, ultrasonic sensor, and a camera esp32, the suggested technique analyses and manages everyday traffic at a three-line intersection. Furthermore, the suggested system achieves three-line intersection sync and implements a balance between the number of vehicles on each side and the green light. when Traffic violation the camera will capture the car number and send it to the database by using telegram.
Knowledge, attitude and practices pertaining to healthy lifestyle in prevention and control of chronic diseases: a rapid review

With the emergence of non-communicable diseases (NCDs), healthy lifestyle has become critical to not only reduce the burden of NCDs, but also to prevent complications and severity of these diseases. Knowledge, attitude and practices (KAP) pertaining to healthy lifestyle play an important role in determining the occurrence and progress of NCDs. KAP elements are interrelated and dependent on each other. While awareness campaigns provide necessary information to people on healthy lifestyle, how well this information is utilised is a grey area. The objective of this rapid review is to understand the relationship between KAP elements pertaining to healthy lifestyle among individuals who are at risk of developing NCDs and those who are living with NCDs. For this purpose, a rapid review of articles available on Google scholar and Pubmed was conducted. Articles focusing on KAP elements pertaining to healthy lifestyle from the year 2015-2021 were reviewed. Findings from the reviewed studies suggested that people have knowledge about the harmful effects of unhealthy diet, physical inactivity, tobacco and alcohol. People have a positive attitude towards healthy lifestyle, however, being knowledgeable and having a positive attitude did not translate into practicing healthy lifestyle due to multiple barriers to translate existing knowledge into practice. Since practice scores are poor among people, existing behaviour change strategies must be re-examined. An epistemological analysis of existing human behaviour with identified barriers and facilitators to adopt healthy lifestyle can help in formulating sustainable and effective social and behaviour change interventions.
How Cannabis Use Status Affects Responses to Anti-Cannabis Messages

Background: The current study examined how cannabis use status impacts cognitive and emotional reactions to public health campaigns about cannabis, and the degree to which these reactions influence message likeability and attitudes about cannabis-related harms. Methods: In a between-subjects design, 252 subjects recruited via Amazon Mechanical Turk viewed six real-world cannabis education messages: three message themes (cognitive ability, driving, and health harms) from each of two real-world public campaigns. Subjects answered questions measuring their cognitive and emotional reactions to each message as well as message likeability and harm perceptions of cannabis. Analyses examined the mediating effects of message responsiveness on the association between baseline cannabis use (user vs non-user) with indices of liking and harm. Results: For all three message themes, informativeness ratings mediated the effect of cannabis user status on the outcomes of perceived harmfulness and message likeability. Specifically, cannabis users perceived cannabis as less harmful and reported all messages as less likeable compared to non-users, partly because they perceived the messages to be less informative than non-users. Surprisingly, users found some of the messages to be more pleasant, which was associated with increased perceptions of harm and message liking compared to non-users. Conclusions: Cannabis education campaigns that take into account differences in emotional and cognitive reactions by use experience, rather than use a “one size fits all” approach, could possibly maximally impact likeability and harm perceptions of these messages.
A study on a novel system for soybean seeds drying: Performance and seed quality

Abstract The proper choice of the dryer configuration for seeds drying is essential to preserve their quality during this process. Drying of soybean seeds was studied in this work using a non-conventional configuration of rotary dryer, named as roto-aerated dryer. The effects of temperature and velocity of the drying air on drying performance and on the capacity of germination and physical integrity of soybean seeds have been quantified. The results shown that this novel rotary dryer can be successfully used for seeds drying. The average residence time (between 22.2 and 24.9 min) was very short compared to other drying systems used for seeds. Conditions with appropriate drying performance and high quality of seeds have been identified.
Design and implementation of low sidelobe substrate integrated waveguide longitudinal slot array antennas

Low sidelobe longitudinal slot array antennas are investigated based on substrate integrated waveguide (SIW) technology. The design method consists of the characterisation of the radiating element, the synthesis of the linear array and the development of the planar array including a feeding power divider by performing full-wave electromagnetic simulations for the final accurate design. Two planar slot array antennas are fabricated with a normal printed circuit board (PCB) process. Low sidelobe features are verified by the measured results which are in agreement with the simulated results. For an 8 times 8 SIW slot array antenna, the measured sidelobe levels (SLLs) are below -36 dB in the H-plane and below -25 dB in the E-plane with the Gain of 20.3 dB at 9.9 GHz. And for a 16 times 16 antenna, the SLLs are below -30 dB both in the E-plane and H-plane with the Gain of 24.4 dB at 10 GHz. The achieved design goals on the radiation patterns therefore validate the proposed low sidelobe SIW slot array antennas to be valuable candidates for high performance communication and radar applications.
Thermal efficiency enhancement of parabolic trough receivers using synthesized graphene oxide/SiO2 nanofluid and a rotary turbulator

ABSTRACT Enhancing the thermal efficiency of parabolic solar collectors is still a challenging issue. In this study, two strategies including using nanofluid and a rotary turbulator are applied to ameliorate thermal efficiency. Here, graphene oxide/SiO2 nanofluids are fabricated in different concentrations and their thermophysical properties are measured. Subsequently, a numerical model is carried out to study the influence of applying nanofluid and the rotary turbulator on thermal efficiency. The results indicate that using nanofluid leads to a 9% increase in thermal efficiency and adding the turbulator enhances thermal efficiency by more than 20%. Although hydraulic analysis reveals the Performance Evaluation Criteria remains in an acceptable range, the friction factor and Nusselt number increase by the addition of nanoparticles to the basefluid.
Simulation of room acoustics to train some aspects of human echolocation

Human echolocation is a technique that could improve the quality of life of most people who suffer from a visual impairment. This method consists of navigating the surroundings through the information provided by the echoes. These echoes are the reflections of sounds deliberately produced by the person who is trying to echolocate. This thesis is divided in a literature study and an implementation to simulate room acoustics to train some aspects of echolocation. The literature study includes three main topics. First of all sound localization is detailed, however, being this theme a large subject of study, only those factors that are involved in human echolocation are explained. These parameters are, among others: sound localization cues, the role of the pinnae and head related transfer functions, and the precedence effect. Secondly, a review of human echolocation most important cues and characteristics is given. To finish the theoretical part of the thesis, an explanation of the chosen method to simulate room acoustics is given. The model used in this thesis is the image method. Finally, some simulations and examples of the proposed model are provided. In addition to these simulations the results of a user test are also showed. These results were evaluated using an exact binomial test.
Ann-Based Modelling of Fly Ash Compaction Curve / Modelowanie Krzywej Zageszczalnosci Popiołu Lotnego Za Pomoca Ssn

Abstract The use of fly ash as a material for earth structures involves its proper compaction. Fly ash compaction tests have to be conducted on separately prepared virgin samples because spherical ash grains are crushed during compaction, so the laboratory compaction procedure is time-consuming and laborious. The aim of the study was to determine the neural models for prediction of fly ash compaction curve shapes. The attempt of applying the artificial neural networks type MLP was made. ANN inputs were new-created variables - principal components dependent on grain-size distribution (as D10-D90 and uniformity and curvature coefficients), compaction method, and fly ash specific density. The output vectors were presented by co-ordinates of generated compaction curve points. Each point (wi, ρdi) was described by two independent ANNs. Using ANN-based modelling method, models which enable establishing the approximate compaction curve shape were obtained. Streszczenie Wykorzystanie popiołu lotnego do konstrukcji ziemnych wymaga jego własciwego zageszczenia. Zageszczanie powoduje wzrost gestosci gruntu, zwieksza jego wytrzymałosc i zdolnosc do przenoszenia obciazen, a takze zmniejsza scisliwosc i przepuszczalnosc. Oznaczenie zageszczalnosci popiołu lotnego musi byc przeprowadzane na próbkach jednokrotnie zageszczanych, poniewaz sferyczne ziarna popiołu sa niszczone w trakcie ubijania, w zwiazku z tym, laboratoryjne ustalenie krzywej zageszczalnosci popiołu jest bardzo czasochłonne. Celem artykułu było wykorzystanie modelowania neuronowego do prognozy kształtu krzywej zageszczalnosci popiołu lotnego. Podjeto próbe zastosowania sztucznych sieci neuronowych SSN typu MLP do opisu punktów krzywej zageszczalnosci. Kazdy punkt krzywej (wi, ρdi) został opisany przez dwie niezalezne SSN. Wykorzystano SSN o róznych wejsciach, którymi były nowo utworzone zmienne - składowe główne, zalezne od uziarnienia (srednic efektywnych d10-d90 oraz wskazników jednorodnosci i krzywizny uziarnienia), metody zageszczenia oraz gestosci własciwej szkieletu gruntowego. Wektorami wyjscia były współrzedne punktów krzywej zageszczalnosci popiołu lotnego. Najlepszymi sieciami neuronowymi były sieci o topologii: 6-3-1, 6-2-1 i 6-4-1 dla prognozy wartosci wilgotnosci wi, oraz 5-3-1 i 6-3-1 dla predykcji wartosci gestosci objetosciowej szkieletu gruntowego ρdi. Uzyskano sieci neuronowe o zadowalajacej precyzji, szczególnie w przypadku wartosci ρdi. Modelowanie krzywej za pomoca SSN umozliwiło ustalenie przyblizonego kształtu krzywej zageszczalnosci popiołu lotnego.
Assessing Antiracism as a Learning Outcome in Social Work Education

The current political climate and reversals of gains made during the Civil Rights Movement underscore the urgent need for preparing emerging social workers to effectively address white supremacy in social work practice. Antiracism education in social work aims to ensure competent antiracist social work practice towards the goal of eradicating white supremacy in all its forms. Given the widening racial disparities evident in income, health and educational outcomes, it is essential to examine the degree to which social work education adequately prepares emerging social work practitioners to engage in antiracist social work practice. This paper presents findings of a systematic review of social work research assessing antiracism as a learning outcome. After reviewing more than 150 studies published between 2008 and 2018, none of them focused on assessing antiracism as a learning outcome. Our review demonstrates that despite the importance of antiracist social work practice, published research on assessment of antiracism as a learning outcome is sparse and is not antiracism-focused as much as it is antiracism-inclusive. More attention to identifying and disseminating best practices for assessing student competence in antiracism practice is required to defeat white supremacy.
Hot-carrier injection suppression due to the nitride-oxide LDD spacer structure

The hot-carrier effects in silicon nitride lightly doped drain (LDD) spacer MOSFETs are discussed. It is found that the oxide thickness under the nitride film spacer affects the hot-carrier effects. The thinner the LDD spacer oxide becomes, the larger the initial drain current degradation becomes at the DC stress test and the smaller the stress time dependence becomes. After the DC stress test, reduced drain current recovers at room temperature. These phenomena are due to the large hot-carrier injection into the LDD nitride spacer, because the nitride film barrier height is much less than the silicon oxide barrier height. Therefore, it is necessary to form the LDD spacer oxide, in order to suppress the large hot-carrier injection in the nitride film LDD spacer MOSFET. The drain current shift mechanism in the nitride spacer MOSFETs is discussed, considering the lucky electron model. >
Dynamic measurements on bridges: design, rehabilitation and monitoring

This paper aims to stress the relevant role that the experimental assessment of the dynamics of bridges can play during the phases of conception and design, construction, reception, rehabilitation and monitoring (temporary and long-term). The importance of the experimental approach in these phases is essentially derived from experience gained from involvement in dynamic tests on several bridges: the Jindo (South Korea), Infante D. Henrique, Vasco da Gama, Luiz I (Portugal) and Z-24 (Switzerland) bridges.
Using deep generative neural networks to account for model errors in Markov chain Monte Carlo inversion

Most geophysical inverse problems are nonlinear and rely upon numerical forward solvers involving discretization and simplified representations of the underlying physics. As a result, forward modeling errors are inevitable. In practice, such model errors tend to be either completely ignored, which leads to biased and over-confident inversion results, or only partly taken into account using restrictive Gaussian assumptions. Here, we rely on deep generative neural networks to learn problem-specific low-dimensional probabilistic representations of the discrepancy between high-fidelity and low-fidelity forward solvers. These representations are then used to probabilistically invert for the model error jointly with the target geophysical property field, using the computationally-cheap, low-fidelity forward solver. To this end, we combine a Markov-chain-Monte-Carlo (MCMC) inversion algorithm with a trained convolutional neural network of the spatial generative adversarial network (SGAN) type, whereby at each MCMC step, the simulated low-fidelity forward response is corrected using a proposed model-error realization. Considering the crosshole ground-penetrating radar traveltime tomography inverse problem, we train SGAN networks on traveltime discrepancy images between: (1) curved-ray (high fidelity) and straight-ray (low fidelity) forward solvers; and (2) finite-difference-time-domain (high fidelity) and straight-ray (low fidelity) forward solvers. We demonstrate that the SGAN is able to learn the spatial statistics of the model error and that suitable representations of both the subsurface model and model error can be recovered by MCMC. In comparison with inversion results obtained when model errors are either ignored or approximated by a Gaussian distribution, we find that our method has lower posterior parameter bias and better explains the observed traveltime data. Our method is most advantageous when high-fidelity forward solvers involve heavy computational costs and the Gaussian assumption of model errors is inappropriate. Unstable MCMC convergence due to nonlinearities introduced by our method remain a challenge to be addressed in future work.
Centerline Planning of Virtual Endoscopy Using B-Snake Model

A novel centerline planning algorithm for virtual endoscopy is proposed. It can be executed in original images data directly, no longer subjects to the results of segmentation. It is based on B-Snake model, and a moving polyhedron centralize method is defined, which provides a centralizing force as the external force of B-Snake model. And the internal force of the Snake model is removed, so as to control the model easily. With the characteristic of B-spline, a few control points are chosen and the smooth and continuous centerline is got effectively. Error analysis and experimental results show the validity of the proposed method.