Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Vehicle Speed at Night Based on Rear Lights

All Speed violations are common things. One way to prevent it is detecting the vehicle and estimating the speed of vehicle that is running. To detect the vehicle, we can use the vehicle lights. The rear lights of the vehicle could be detected easier than the front lights of the vehicle because the color of the rear lights was different than the common lights on the road at night. This paper focuses on estimating the speed of a vehicle that runs at night based on the rear lights of a vehicle. The first step is calibrating the camera used and extracting the video into frames. Then we tracked the vehicle position in each frame in the Region of Interest (ROI). The next step is bounding and labelling each detected vehicle. After that, the conversion from the camera angle and the camera height become the actual distance in each frame. The final step is to find the movement of the vehicle from one frame to another using Euclidean distance to determine the speed of the vehicle. This research results in high estimation accuracy when in the ROI II area for the actual vehicle speed between 20-40 km/h, and for vehicle with actual speed between 40-60 km/h, the accuracy was high if it is in the ROI III area.
El estrés docente en tiempos de pandemia.

COVID-19 caused an abrupt life change that placed all of humanity in a critical state. The social confinement forced the majority of the population to change their daily activities. The exterior posed a threat to our health and that of our loved ones. Adapting to the new normal was a difficult process. One of the social groups that suffered this change was the teaching profession, who with a broken educational system and using their own resources, looked for a way to continue their educational work with the implementation of distance education. The use of technology for some was a real challenge, even generating depression, anxiety, and stress.
Noise Reduction from L-Band ALOSPALSAR Data Set Using Spatial Domain Gaussian Low-Pass Filter

The microwave remote sensing system is the broadest tool use to get information about the earth's objects in the form of images. These images are very much affected by various noises, which affects the precision of the images. These, in turn, affect the information present in it. To improve, the quality of satellite images, denoising of the image is an essential task. Gaussian noise majorly seen in all remotely sensed images. Natural sources cause this noise during the acquisition of the image. Many Researchers used Numerous types of filtering techniques to reduce such noises. In this paper, we have focused on lowering Gaussian noise from the microwave satellite dataset. To minimise Gaussian noise, the spatial domain Gaussian Low Pass Filter (GLPF) with different window sizes applied to a single Polarized (HH) L-band ALOS PALSAR satellite SLC level-1 Datasets. The results of filtered images are evaluated based on the respective mean, standard deviation and coefficient of variance.
Sentence Similarity Calculation Based on Probabilistic Tolerance Rough Sets

Sentence similarity calculation is one of the important foundations of natural language processing. The existing sentence similarity calculation measurements are based on either shallow semantics with the limitation of inadequately capturing latent semantics information or deep learning algorithms with the limitation of supervision. In this paper, we improve the traditional tolerance rough set model, with the advantages of lower time complexity and becoming incremental compared to the traditional one. And then we propose a sentence similarity computation model from the perspective of uncertainty of text data based on the probabilistic tolerance rough set model. It has the ability of mining latent semantics information and is unsupervised. Experiments on SICK2014 task and STSbenchmark dataset to calculate sentence similarity identify a significant and efficient performance of our model.
Creep damage and fracture of notched specimens under static and fast periodic loading

Creep-damage processes in notched specimens subjected to static and periodic loading are studied experimentally. Subsequent simulations were carried out using Rabotnov evolution equation. In case of dynamic creep (fast periodic loading) the constitutive equations derived by use of asymptotic and averaging methods were used. Numerical results were obtained by the combination of FEM and predictor-corrector time integration scheme. Fracture process was studied using designed numerical procedure based on the elimination of the finite elements with critical values of damage parameter. The fracture times and directions of the crack propagation were determined. The qualitative difference between processes of crack propagation in cases of static and periodic loading were observed.
Global Philanthropy: Does Institutional Context Matter for Charitable Giving?

In this article, we examine whether and how the institutional context matters when understanding individuals’ giving to philanthropic organizations. We posit that both the individuals’ propensity to give and the amounts given are higher in countries with a stronger institutional context for philanthropy. We examine key factors of formal and informal institutional contexts for philanthropy at both the organizational and societal levels, including regulatory and legislative frameworks, professional standards, and social practices. Our results show that while aggregate levels of giving are higher in countries with stronger institutionalization, multilevel analyses of 118,788 individuals in 19 countries show limited support for the hypothesized relationships between institutional context and philanthropy. The findings suggest the need for better comparative data to understand the complex and dynamic influences of institutional contexts on charitable giving. This, in turn, would support the development of evidence-based practices and policies in the field of global philanthropy.
Pride and Prejudice

The concept of graduate employability has gained great prominence in international education. However, there still exists a gap in sexual orientation discrimination in graduate employability among transgender and queer (TQ) international students. In our qualitative study investigating graduate employability of transgender and queer students graduating from Australian and Canadian institutions, we have interviewed 14 international graduates with transgender and queer identity regarding their perceptions of sexual orientation and recruitment discrimination at the workplaces. Utilizing intersectionality as a conceptual framework, we have studied employability-related problems that these marginalized students with their foreigner identities have experienced in the labor market. The findings will be around the social, cultural, and political impacts of Canadian and Australian working and recruitment environments on the varying extent of discrimination, namely local attitudes toward queer and transgender international graduates, the manifestation of antidiscrimination laws, and the extent to which employers value stereotypically male heterosexual personality traits.
Effect of monomeric sequence on nanophase-segregated structure and water transport in Nafion 117

Introduction Nafion is a polyelectrolyte consisting of nonpolar N=(CF2-CF2) and polar-ionizable P=(O-CF2-CF(CF3))2-CF2-CF2-SO3H) segment. Hydrated Nafion 117 with an average of 6.5 hydrophobic tetrafluoroethylene units per vinyl ether sulfonate unit is widely used for membranes in fuel cell operations due to its relatively high proton conductivity, mechanical and electrochemical stability. Although the ratio of hydrophobic to hydrophilic monomers is well known, the monomeric sequence in the Nafion polymer is not well characterized and its affect has not been included as a design variable to improve fuel cell performance. There is a general consensus supported by experiments and simulations that hydrated Nafion has a nanosegregated structure consisting of hydrophobic and hydrophilic domains. Notwithstanding, the details of the segregated morphology remain unclear and highly debated. In this work we explore in particular the effect of monomer sequence on the structure and transport in hydrated Nafion 117 by Molecular Dynamics (MD) simulations. Our goal is to elucidate the effect of the monomeric sequence of polar (P) and nonpolar (N) monomeric units in Nafion on the phase-segregated morphology and water/hydronium transport.
Impact of COVID-19 on Performance Evaluation Large Market Capitalization Stocks and Open Innovation

This research is an event study that evaluates the performance of large market capitalization shares using a performance model that is adjusted to risks due to the COVID-19 outbreak. The study measured the performance of large market capitalization stocks which represented each tick size on the Indonesian Stock Exchange during the COVID-19 pandemic using the Sharpe Index, the Treynor Ratio, and Jensen’s Alpha. The sample selection used a purposive sampling technique and 24 stocks were selected as samples in the study. We used the daily closing price of stocks, the Indonesia composite index, and average risk-free rate return (BI rate). By using Jensen’s Alpha, this study found that FREN was the highest beta with a value of 1.8189, indicating that the index was an effective and well-diversified stock. FREN is low priced and the highest market capitalization stock in its tick size (third tier stocks). Jensen’s Alpha is good for measuring the performance of large capitalization and low-priced stocks. There are eight stocks that always have negative values in each method of measuring stock performance, which indicates that these stocks underperformed during COVID-19.
Prediction of the viscosity of water-in-oil emulsions

ABSTRACT Water-in-oil (W/O) emulsions occur in different parts of petroleum recovery and since their properties are different from those of crude oil and water, it is essential to find information about their physical properties. The water causes some problems and heightens the cost of oil production. Separation of water from oil, water treatment, and disposal of water are the steps that make oil production costly. Among various physical properties of W/O emulsions, viscosity is the most important. Because of droplet crowding, emulsions indicate non-Newtonian behavior, which is why their viscosity is higher than the viscosity of oil and water. As a result, it is essential to have knowledge about the viscosity of W/O emulsions in order to extract and process petroleum properly. In this study, an intelligent model, namely a COA-LSSVM, is presented to accurately prognosticate the viscosity of W/O emulsions. The results indicated that the values estimated by the developed model is in great consistency with the laboratory data by R2 of 0.9972 and MSE of 1.1762 * 10−5. A comparison with another model, which was recently introduced, revealed that the developed model in this study is superior.
On the Relationship of a Low-Level Jet and the Formation of a Heavy-Rainfall-Producing Mesoscale Vortex over the Yangtze River Basin

Dabie vortices (DBVs) are a type of heavy-rainfall-producing mesoscale vortices that appear with a high frequency around the Dabie Mountain over the Yangtze River Basin. For a long time, scholars have found that DBVs tend to form when a low-level jet (LLJ) appears in their neighboring regions. However, the underlying mechanisms of this phenomenon still remain vague. This study furthers the understanding of this type of event by conducting detailed analyses on a long-lived eastward-moving DBV that caused a severe flood in the 2020 summer. It is found that the LLJ in this event was belonged to a nocturnal LLJ type, with its maximum/minimum appeared around 2100/0600 UTC. The diurnal cycle of LLJ affected precipitation and intensity of the DBV notably: As the LLJ intensified, vortex’s precipitation and intensity both enhanced, and vice versa. The LLJ exerted two effects on the DBV’s formation that are opposite to each other. The more important effect is that the LLJ caused intense lower-level convergence around its northern terminus. This convergence directly produced cyclonic vorticity through vertical stretching, which dominates the DBV’s formation and enhances the convection-related upward cyclonic vorticity transport that acted as another favorable factor. The less important effect is that (i) the LLJ induced import of anticyclonic vorticity into the vortex’s central region, which decelerated the DBV’s formation; and (ii) the LLJ-related to strong ascending motions tilted horizontal vorticity into negative vertical vorticity, which reduced the growth rate of cyclonic vorticity.
Meta-Heuristic Firefly Algorithm Based Optimal Design of Passive Harmonic Filter for Harmonic Mitigation

In present years, the increasing use of power electronic based nonlinear devices in various power system applications causes harmonic distortions in source side voltages and currents. The unwanted harmonic distortion can introduce detrimental effects on electrical equipment and temperature increases in neutral conductors and distribution transformers. In this paper, a new optimization method of Meta-heuristic firefly algorithm is proposed for the optimal design of passive harmonic filters to mitigate the harmonics existing in the power system environment. The performance of the proposed firefly algorithm based passive filter evidences better result compared with the other algorithm based methods.
Three-Stage Stackelberg Long-Term Incentive Mechanism and Monetization for Mobile Crowdsensing: An Online Learning Approach

Recently, crowdsensing revolutionizes sensing paradigm in Internet of Things (IoTs). However, a practical incentive mechanism which works for time-varying scenario and fairly incentivizes users to participate in crowdsensing is less studied. In this paper, we propose an incentive mechanism for crowdsensing under continuous and time-varying scenario using three-stage Stackelberg game. In such a scenario, different requesters generate sensing tasks with payments to the platform at each time slot. The platform makes pricing decision to determine rewards for tasks without complete information, and then notifies task-price pairs to online users in Stage I. In Stage II, users select optimal tasks as their interests under certain constraints and report back to the platform. The platform fairly selects users as workers in order to ensure users’ long-term participation in Stage III. We use Lyapunov optimization to address online decision problems for the platform in Stage I and III where there are no prior knowledge and future information available. We propose an FPTAS for users to derive their interests of tasks based on their mobile devices’ computing capabilities in Stage II. Numerical results in simulations validate the significance and superiority of our proposed incentive mechanism.
Strategies of Combating Corruption in Nigeria: The Islamic Perspective

Corruption has become a pandemic and a moral burden that bedeviled the world. Corruption is a practice that permeates all societal segments, sectors, activities and it takes various shapes, forms, dimensions, classifications and manifestations. Nearly all countries of the world are affected by the endemic of corruption albeit, in different magnitude with some having lower incidence while others a high rate of occurrence. Corruption has constituted itself as a political and socioeconomic problem worldwide because it leads to misgovernance, deprivation, inequality and squander of scarce public resources which have the concomitant repercussions of poverty, insecurity, joblessness, hunger and malnutrition, poor healthcare services delivery and low quality education. The problem of corruption took a new dimension when it continuously defies various strategies and alternatives proffered by nation-states, international organisations and agencies. One of the outstanding measures that permanently proves workable is the Islamic model which forbids the practice of corruption and ties it with moral burden and spiritual integrity. Hence, this study presented an alternative model with reference to Nigeria as the area of study. The study utilised a conceptual descriptive approach where documented sources were consulted and analysed. The study discovered that despite the existence of various anti-graft agencies, corruption continues in Nigeria unabated which portrays the need for adopting a different approach and that has been provided as the Islamic perspective. The study recommends among several others that the Islamic ethics and morals of detesting corrupt practices and the accompanied sanctions should be integrated in the crusade against corruption.
Controlling COVID-19 Propagation with Quarantine of Influential Nodes

We study importance of influential nodes in spreading of epidemic COVID-19 in a complex network. We will show that quarantine of important and influential nodes or consider of health protocols by efficient nodes is very helpful and effective in the controlling of spreading epidemic COVID-19 in a complex network. Therefore, identifying influential nodes in complex networks is the very significant part of dependability analysis, which has been a clue matter in analyzing the structural organization of a network. The important nodes can be considered as a person or as an organization. To find the influential nodes we use the technique for order preference by similarity to ideal solution (TOPSIS) method with new proposed formula to obtain the efficient weights. We use various centrality measures as the multi-attribute of complex network in the TOPSIS method. We define a formula for spreading probability of epidemic disease in a complex network to study the power of infection spreading with quarantine of important nodes. In the following, we use the Susceptible–Infected (SI) model to figure out the performance and efficiency of the proposed methods. The proposed method has been examined for efficiency and practicality using numerical examples.
Joint Optimization of Spectral Efficiency and Energy Harvesting in D2D Networks Using Deep Neural Network

In this work, we study the joint optimization of energy harvesting and spectrum efficiency in wireless device-to-device (D2D) networks where multiple D2D pairs adopt simultaneous wireless information and power transfer (SWIPT) functionality with a power-splitting policy. To observe the trade-off relationship between spectrum efficiency and energy harvesting via SWIPT, we construct an objective function using the weighted sum method, which scalarizes the dominant with spectrum efficiency and energy harvesting, and attempt to find the optimal transmit power and power-splitting ratio to maximize the objective function. Typical iterative search algorithms such as exhaustive search (ES) or gradient search (GS) with a log barrier function are employed to find the global optimum and sub-optimum, respectively. Furthermore, we apply a deep neural network (DNN) learning algorithm to deal with the non-convexity of the objective function with an effective loss function. The simulation results verify the trade-off relationship between spectrum efficiency and energy harvesting, and show that the DNN-based algorithm can achieve a near-global optimal solution with computational complexity much lower than that of the optimization-based iterative algorithms.
Rural and urban primary school heads’ perspectives on staff appraisal systems in Zimbabwe: A phenomenological approach

The study sought to explore rural and urban primary school heads’ perspectives on staff appraisal systems in Masvingo Province, Zimbabwe and utilised a qualitative approach based on a phenomenological design. Five rural primary school heads and five urban primary school heads with at least 14 years of experience were purposely selected. In-depth, face-to-face phenomenological interviews were conducted to collect the lived experiences of participants in relation to performance appraisal systems for teachers. Data were transcribed verbatim and analysed thematically. Three major themes emerged: (a) implementation of performance appraisals; (b) feedback and professional development; and (c) challenges with regard to the implementation of performance appraisals. The economic crisis in Zimbabwe has had a negative impact on school performance appraisal systems. Both school locations had implemented such systems, but were faced with limited resources, time constraints and teachers’ lack of interest in being promoted. In looking at the differences between rural and urban school head participants, the focus was on observation and feedback processes. This study has important implications for staff appraisal systems in Zimbabwe.
The Effect of Co-Deposition of SiC Sub-Micron Particles and Heat Treatment on Wear Behaviour of Ni–P Coatings

The purpose of the study is to assess the influence of SiC particles and heat treatment on the wear behaviour of Ni–P coatings when in contact with a 100Cr6 steel. Addition of reinforcing particles and heat treatment are two common methods to increase Ni–P hardness. Ball-on-disc wear tests coupled with SEM investigations were used to compare as-plated and heat-treated coatings, both pure and composite ones, and to evaluate the wear mechanisms. In the as-plated coatings, the presence of SiC particles determined higher friction coefficient and wear rate than the pure Ni–P coatings, despite the limited increase in hardness, of about 15%. The effect of SiC particles was shown in combination with heat treatment. The maximum hardness in pure Ni–P coating was achieved by heating at 400 °C for 1 h while for composite coatings heating for 2 h at 360 °C was sufficient to obtain the maximum hardness. The difference between the friction coefficient of composite and pure coatings was disclosed by heating at 300 °C for 2 h. In other cases, the coefficient of friction (COF) stabilised at similar values. The wear mechanisms involved were mainly abrasion and tribo-oxidation, with the formation of lubricant Fe oxides produced at the counterpart.
Durability of Gerber Saddles in RC Bridges: Analyses and Applications (Musmeci Bridge, Italy)

Guaranteeing adequate safety levels in critical infrastructures such as bridges is essential to modern societies and their vital services. Bridges with reinforced concrete structures are subject to deterioration, especially due to corrosion effects. Gerber saddles are among the key components of bridges which are especially exposed to environmental actions due to their position and reduced possibility of inspection. In this paper, a framework for the durability analysis of these components is proposed, considering the simultaneous presence of permanent loads and environmental actions under the form of chloride ions. Nonlinear numerical simulations adopting the finite element code ATENA are performed, accounting for chloride ingress analyses. The presence of cracks (due to applied loads and/or design/construction defects) which may speed-up corrosion propagation, steel reinforcement loss, cracking and spalling, and their effects on the load-bearing capacity is considered. This framework has been applied to the Gerber saddles of a prominent reinforced concrete (RC) bridge, namely the Musmeci bridge in Potenza, Italy. Durability analyses made it possible to evaluate the saddles’ strength capacity (i) at the time of construction, (ii) after forty-five years since the construction, and (iii) at an extended time of fifty years. The results show that corrosion can influence both the ultimate load capacity and the collapse mechanism.