Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Particulate Organic Carbon Deposition on Nitrate Reduction in the Hyporheic Zone

The hyporheic zone (HZ), where surface water and groundwater interact in sediments beneath streams, presents unique conditions for nutrient dynamics, such as carbon and nitrogen (N) cycling. Organic carbon (OC) in aquatic systems has two distinct forms: dissolved organic carbon and particulate organic carbon (POC). OC affects N reduction and controls the occurrence of denitrification, a primary process by which nitrate (NO3−) is removed as gas (N2 or N2O) to the atmosphere. When POC is the predominant form of OC in streams, how POC influences NO3− reduction within a HZ remains unclear. We established a new reactive transport model incorporating POC transport and filtration processes to assess how POC deposition affects NO3− removal. Sediment permeability decreases as POC is filtrated in the streambed. Nitrate influx is reduced due to POC‐induced sediment clogging. The deposited POC in the shallow streambed serves as an OC source and supports higher biogeochemical reaction kinetics because OC is an important substrate promoting microbial activities. The filtrated POC pool enhances denitrification, thus causing a higher NO3− removal efficiency. In contrast, the POC pool is typically assumed to be distributed homogeneously in sediments, potentially causing a significant underestimation of stream‐borne NO3− removal. A lower hydraulic conductivity, a smaller POC grain size, and a larger POC filtration efficiency and in‐stream POC concentration allow for extensive POC deposition within the shallow streambed that favors nitrate removal. This study provides a better understanding of N processing and an accurate estimation of NO3− removal potential in HZs.
Truth finder algorithm based on entity attributes for data conflict solution

: The Internet now is a large-scale platform with big data. Finding truth from a huge dataset has attracted extensive attention, which can maintain the quality of data collected by users and provide users with accurate and efﬁcient data. However, current truth ﬁnder algorithms are unsatisfying, because of their low accuracy and complication. This paper proposes a truth ﬁnder algorithm based on entity attributes (TFAEA). Based on the iterative computation of source reliability and fact accuracy, TFAEA considers the interactive degree among facts and the degree of dependence among sources, to simplify the typical truth ﬁnder algorithms. In order to improve the accuracy of them, TFAEA combines the one-way text similarity and the factual conﬂict to calculate the mutual support degree among facts. Furthermore, TFAEA utilizes the symmetric saturation of data sources to calculate the degree of dependence among sources. The experimental results show that TFAEA is not only more stable, but also more accurate than the typical truth ﬁnder algorithms.
An improved initialization center k-means clustering algorithm based on distance and density

Aiming at the problem of the random initial clustering center of k means algorithm that the clustering results are influenced by outlier data sample and are unstable in multiple clustering, a method of central point initialization method based on larger distance and higher density is proposed. The reciprocal of the weighted average of distance is used to represent the sample density, and the data sample with the larger distance and the higher density are selected as the initial clustering centers to optimize the clustering results. Then, a clustering evaluation method based on distance and density is designed to verify the feasibility of the algorithm and the practicality, the experimental results on UCI data sets show that the algorithm has a certain stability and practicality.Aiming at the problem of the random initial clustering center of k means algorithm that the clustering results are influenced by outlier data sample and are unstable in multiple clustering, a method of central point initialization method based on larger distance and higher density is proposed. The reciprocal of the weighted average of distance is used to represent the sample density, and the data sample with the larger distance and the higher density are selected as the initial clustering centers to optimize the clustering results. Then, a clustering evaluation method based on distance and density is designed to verify the feasibility of the algorithm and the practicality, the experimental results on UCI data sets show that the algorithm has a certain stability and practicality.
KLINIKINIAI ORTODONTINĖS EKSTRUZIJOS DANTŲ RESTAURACINIO GYDYMO ASPEKTAI

Tikslas. Šia literatūrine apžvalga siekiama surinkti infor­maciją apie klinikines indikacijas ir kontraindikacijas, rasti ortodontinės ekstruzijos (OE) atlikimo metodiką, išsiaiškinti jos privalumus ir trūkumus, siekiant pailginti dantų vainikėlius ar sukurti vietą implantams. Medžiaga ir metodai. Literatūros apžvalgai buvo ieš­koma straipsnių, paskelbtų 2017-2022 m. , parašytų an­glų kalba, naudojant šiuos raktažodžių derinius PubMed ir Cochrane duomenų bazėse anglų kalba: (orthodon­tic extrusion OR forced eruption) AND (restoration OR implant OR crown lengthening)) (liet. (ortodontinė eks­truzija AR priverstinis daiginimas) IR (restauracija AR implantas AR vainikėlio prailginimas)). Google Scholar paieškos sistemoje buvo atlikta rankinė paieška. Rezultatai. Atrinkti 9 moksliniai straipsniai, atitinkantys raktažodžius ir literatūros apžvalgai keliamus kriterijus. Naudojant ortodontinę ekstruziją, išsaugomas atraminis kaulas, užtikrinama geresnė estetika, išsaugojami gre­timi dantys ir sumažinamas neigiamas poveikis vainiko ir šaknies santykiui. Kaip neinvazinis metodas, galintis padidinti turimą kaulo ir minkštųjų audinių tūrį, OE yra patraukli ir perspektyvi vietos kūrimo implantui meto­dika. Norint pasiekti norimą ekstruziją, galima naudoti tiek nenuimamus, tiek nuimamus prietaisus. Išvados. OE yra efektyvi gydymo priemonė pacientams, kuriems reikalingas vainiko prailginimas ar vietos im­plantams paruošimas. Atliekant šią procedūrą, būtinas išsamus paciento ištyrimas, kompleksinio gydymo pla­navimas ir atlikimas tiksliai pagal indikacijas. Norint išsiaiškinti ilgalaikį ortodontiškai ekstruduotų dantų ir su jais judėjusių minkštųjų ir kietųjų audinių stabilumą, reikalingi tolesni tyrimai.
Pre-service teachers applying culturally-responsive critical pedagogy

How do pre-service teachers practice culturally-responsive critical pedagogy in the classroom? This study of two pre-service teachers investigated a professional practice placement that followed theory courses. The research found five applications of culturally-responsive critical pedagogy: reflecting on self and identity; centring ākonga (learners); uplifting culture; developing critical consciousness; and improving academic outcomes for non-dominant culture learners. However, the pre-service teachers placed varied emphasis on those applications, including not engaging with the achievement application. The research offers concrete practice examples of culturally-responsive critical pedagogy by pre-service teachers, and questions the assumption that novice pre-service teachers have the skills to enact theory-to-practice.
Monitoring and Recognition of Heart Health using Heartbeat Classification with Deep Learning and IoT

The advancement and innovations in the field of science and technology paved way for various advanced treatments in the field of medicine. They are implemented using sensors, and computer-aided designs with artificial intelligence techniques. This helps in the detection of serious health constraints at an earlier stage with appropriate treatments using decision-making techniques. One of the important health concerns that are increasing rapidly is cardiovascular disorders. This includes Arrhythmia and Myocardial Infarction. Earlier prediction and classification can protect them from serious constraints. They are diagnosed using the Electrocardiogram (ECG). To obtain accurate results, artificial intelligence techniques are implemented to extract the optimum output. The proposed system includes the detection and classification using deep learning techniques with the Internet of Things (IoT). The existing heartbeat detection system is overcome using a deep convolutional neural network. This helps in the implementation of automatic heartbeat detection and identification of abnormalities. The ECG signals are pre-processed with segmentation and feature extraction techniques. The classification and identification of constraints in the functioning of the heart are identified using optimization algorithms. The proposed system is trained, tested, and evaluated using the MIT-BIH arrhythmia database. The accuracy and efficiency of the proposed system are 99.98% using the MIT-BIH dataset.
Explaining Image Classifiers by Adaptive Dropout and Generative In-filling

Explanations of black-box classifiers often rely on saliency maps, which score the relevance of each input dimension to the resulting classification. Recent approaches compute saliency by optimizing regions of the input that maximally change the classification outcome when replaced by a reference value. These reference values are based on ad-hoc heuristics such as the input mean. In this work we marginalize out masked regions of the input, conditioning a generative model on the rest of the image. Our model-agnostic method produces realistic explanations, generating plausible inputs that would have caused the model to classify differently. When applied to image classification, our method produces more compact and relevant explanations, with fewer artifacts compared to previous methods.
Critically Analyze the Management Problems Brought about by the Co-production of Multiple Companies

Multi -department cooperation has become a modern development trend. Together with the public sector and the private sector and the volunteer departments, public services not only accelerate efficiency to protect citizen rights and interests, but also help the development of social organizations. Out of the purpose of solving people's problems faster and serving the people, multiple departments work together. However, the values, goals, and the differences of corporate culture have brought many problems. In order to better protect the rights and interests of the people, different organizations need to run in and tolerate each other. This article is committed to solving complex interpersonal relationships, and system management caused by multiple departments. Putting members of different departments and each department seek common ground and mutual respect to promote the public service industry. Research and analyze the organizational goals and incentive methods of various organizations, and help explore managers from the root cause of conflict, resolve contradictions, improve work efficiency, and promote the positive development of the public service industry.
p-Laplace Variational Image Inpainting Model Using Riesz Fractional Differential Filter

In this paper, p-Laplace variational image inpainting model with symmetric Riesz fractional differential filter is proposed. Variational inpainting models are very useful to restore many smaller damaged regions of an image. Integer order variational image inpainting models (especially second and fourth order) work well to complete the unknown regions. However, in the process of inpainting with these models, any of the unindented visual effects such as staircasing, speckle noise, edge blurring, or loss in contrast are introduced. Recently, fractional derivative operators were applied by researchers to restore the damaged regions of the image. Experimentation with these operators for variational image inpainting led to the conclusion that second order symmetric Riesz fractional differential operator not only completes the damaged regions effectively, but also reducing unintended effects. In this article, The filling process of damaged regions is based on the fractional central curvature term. The proposed model is compared with integer order variational models and also GrunwaldLetnikov fractional derivative based variational inpainting in terms of peak signal to noise ratio, structural similarity and mutual information.
Physical-Layer Reliability of Drones and Their Counter-Measures: Full Versus Half Duplex

In this article, we study the advantages and disadvantages that full-duplex (FD) radio technology brings to remote-controlled drone and counter-drone systems in comparison to classical half-duplex (HD) radio technology. We consider especially the physical-layer reliability perspective that has not yet been comprehensively studied. For establishing a solid analytical background, we first derive original closed-form expressions to evaluate demodulation and detection performance of frequency-hopped and frequency-shift keyed drone remote control signals under external or self-inflicted interference. The developed analytical tools are verified by comparison to simulated results and then used to study the impact that the operation mode has on the operable area of drones and effectiveness of counter-drone systems in different scenarios, linking the physical layer performance to practical safety. Analysis of the scenarios shows that FD operation compared to HD can improve the effectiveness of a counter-drone system and that in FD mode a drone can detect the attacks from the counter-drone system from a greater distance than in HD mode. However, two-way communication between the remote controller and drone in FD mode compared to HD significantly reduces the drone’s operable area when targeted by a smart counter-drone system.
On Waveguide Excitation By Source Placed On The Lateral Cross-Section

The problem of excitation of electromagnetic oscillations in a waveguide with metal walls, which has an arbitrary cross-section, is reduced to an infinite set of boundary-value problems for telegraph equations in a quarter of the plane. The values of the longitudinal components of the field or of the lateral components of the magnetic vector (surface currents) on the cross-section of the waveguide can be chosen as the wave sources. It is preliminary shown that the components of the non-harmonic electromagnetic field in the waveguide are expanded into series by two sets of eigen functions of the two-dimensional Laplacian that satisfy the Neumann or Dirichlet boundary conditions. The coefficients of these expansions are the solutions of telegraph equations or derivatives of these solutions. The boundary-value problem for the telegraph equation in a quarter of the plane is considered. It has been established which boundary conditions are sufficient for determining its unique solution. The solvability conditions of the auxiliary over-determined boundary-value problem have been written down. The formulas that give an explicit solution of the telegraph equation in a quarter of the plane in the case of different boundary conditions have been obtained. It is shown how to determine the boundary values of the solutions of the telegraph equations for various types of sources of the electromagnetic field. As an example, the longitudinal components of the field for the high mode of a rectangular waveguide for given pulse sources are determined numerically.
CONNECT: a framework for building effective working relationships with distance education doctoral students and their advisers

Abstract This critical reflection presents the major challenges faced while studying a doctorate through distance education. The challenges faced are explored in relation to the research literature to devise a framework for underpinning the practice of distance education doctoral advisers to ensure effective working relationships are developed and maintained throughout the candidature. The primary aim of the framework, known as CONNECT, is to reduce feelings of isolation for distance education doctoral students, while promoting a sense of connection to advisers, peers, and the wider university environment to reduce attrition rates in this population.
Fecal calprotectin: A marker of Crohn's disease activity

Introduction. Crohn's disease (CD) is a chronic inflammatory bowel disease (IBD) with periods of remission and exacerbation. Numerous studies have been conducted in order to identify the ideal marker when it comes to the inflammatory bowel diseases. In the literature, fecal calprotectin is mentioned as a marker of inflammation. Elevated levels of calprotectin can be detected in stool and they indicate the migration of neutrophils to the intestinal mucosa that occurs with intestinal inflammation. The aim. The main goal of this study was to examine the concentration of fecal calprotectin and CRP depending on the clinical, endoscopic and histological characteristics of patients with Crohn's disease and whether there is a correlation of these markers with disease activity. Methods. The research was conducted in the period from January 2015 to January 2016. The study included subjects who had been diagnosed with Crohn's disease. The study involved 45 respondents, aged 20 - 58 years. All subjects included in the study underwent a colonoscopic examination with biopsy and pathohistological analysis. Fecal calprotectin was determined in one stool sample in all subjects, and that was done quantitatively by a commercial ELISA assay. Determination of serum CRP concentrations was performed in the Central Biochemical Laboratory by standard methods. Results. Fecal concentrations of calprotectin are elevated in all three forms of the disease, while they are significantly higher in moderately severe (545 vs. 218, p ˂ 0.001) and severe forms of the disease (1000 vs. 218, p ˂ 0.001) compared to the mild form. Fecal concentrations of calprotectin are significantly higher at endoscopic grade 3 compared to the other three endoscopic grades (765.3 vs. 314, p ˂ 0.001), (765.3 vs. 392.8, p ˂ 0.001), (765.3 vs. 448.2, p ˂ 0.001). Fecal concentrations of calprotectin are significantly higher in extensive pathological findings compared to normal epithelial surface (1000 vs. 21, p ˂ 0.001) as well as in extensive pathological findings compared to focal pathological findings (1000 vs. 309, p ˂ 0.001). Conclusion. The more severe form of clinical disease activity is accompanied by higher fecal values of calprotectin and higher endoscopic grade, and a more severe histological grade of disease is accompanied by higher fecal values of calprotectin.
K-medoids inverse regression

Abstract K-means inverse regression was developed as an easy-to-use dimension reduction procedure for multivariate regression. This approach is similar to the original sliced inverse regression method, with the exception that the slices are explicitly produced by a K-means clustering of the response vectors. In this article, we propose K-medoids clustering as an alternative clustering approach for slicing and compare its performance to K-means in a simulation study. Although the two methods often produce comparable results, K-medoids tends to yield better performance in the presence of outliers. In addition to isolation of outliers, K-medoids clustering also has the advantage of accommodating a broader range of dissimilarity measures, which could prove useful in other graphical regression applications where slicing is required.
Aurelia: Jurnal Penelitian dan Pengabdian Masyarakat Indonesia

One of the causes of less than optimal employee performance is the ineffective division of workload and not conducive and comfortable work environment. This study aims to determine the effect of workload and work environment on employee performance at PT. Merpati Angkasa Abadi Sultan Muhammad Kaharuddin Airport, Sumbawa. This study used a quantitative research method which was conducted in September 2022 at Sultan Muhammad Kaharuddin Airport, Sumbawa. This research was conducted using sampling techniques and non-probability sampling. Data collection techniques and questionnaires. The data used is primary data obtained from filling out questionnaires by employees. The number of samples in this study were 20 employees. The research instrument was tested for validity by product moment correlation and reliability test by Cronbach Alpha. The data analysis used was multiple linear regression with the help of SPSS version 24. The results showed that the t-count for the effect of workload on employee performance was 4,216 and a sig value of 0,001. The sig value obtained <0.05 means that workload has a positive and significant effect on employee performance. Furthermore, the t-count value of the effect of the work environment on employee performance is 2.149 with a sig value of 0.046 <0.05, meaning that the work environment has a positive and significant influence on employee performance. Then for the simultaneous test (F test) obtained an F-count value of 32,664 and a sig value of 0,000 or a sig value <α (0.05). It was concluded that the performance of PT. Merpati Angkasa Abadi, Sultan Muhammad Kaharuddin, Sumbawa Airport, is affected simultaneously by workload and work environment by 79.4%. This shows that the more workload that is completed, the better the employee's performance. Likewise with the work environment, the more comfortable and conducive the work environment, the better employee performance.
Exponential Smoothing, Long Memory and Volatility Prediction

Extracting and forecasting the volatility of financial markets is an important empirical problem. Time series of realized volatility or other volatility proxies, such as squared returns, display long range dependence. Exponential smoothing (ES) is a very popular and successful forecasting and signal extraction scheme, but it can be suboptimal for long memory time series. This paper discusses possible long memory extensions of ES and finally implements a generalization based on a fractional equal root integrated moving average (FerIMA) model, proposed originally by Hosking in his seminal 1981 article on fractional differencing. We provide a decomposition of the process into the sum of fractional noise processes with decreasing orders of integration, encompassing simple and double exponential smoothing, and introduce a lowpass real time filter arising in the long memory case. Signal extraction and prediction depend on two parameters: the memory (fractional integration) parameter and a mean reversion parameter. They can be estimated by pseudo maximum likelihood in the frequency domain. We then address the prediction of volatility by a FerIMA model and carry out a recursive forecasting experiment, which proves that the proposed generalized exponential smoothing predictor improves significantly upon commonly used methods for forecasting realized volatility.
Legislations Governing E-learning in Jordanian Higher Education Institutions: An Analytical Study

This study aims to analyze the legislation governing e-learning in Jordanian higher education institutions through an analytical, descriptive approach to the contents of the Jordanian legislative texts related to e-learning. This comes to determine the concept and types of e-learning, determine the positive and negative points of the process of employing e-learning in university education and determine the nature of the electronic content, its necessary teaching methods, and the nature of the technical and technological infrastructure necessary for e-learning. E-learning is distinguished by its flexibility; it provides an attractive learning environment, takes into account individual differences, and provides students with many research and scientific skills. On the other hand, it increased social inequality and the inability of parents to follow up on their children due to their electronic illiteracy. E-learning also reduces the opportunities for face-to-face interaction since students are affected by body language during the explanation. The study recommended the need to hold training courses for teachers and students and reconsider the methods of evaluating teachers depending on the quality of employing the electronic methods in their teaching. Received: 09 April 2023 / Accepted: 2 June 2023 / Published: 5 July 2023