Miracle-dz/UniFAR-FTUs huggingface dataset:
Sexual violence in the discourse of digital citizens: Strengthening the concept of digital citizenship as online civic engagement

Cases of sexual violence frequently exposed on social media have sparked intense discussions among digital citizens. This phenomenon has given rise to new patterns of participation in the digital public sphere, indicating progress in efficiently and effectively expressing public aspirations through online civic engagement. Thus, online citizen engagement further strengthens the concept of digital citizenship as an active participation form in the digital world. In this study, the topic modeling method is utilized as a machine learning approach with statistical methods to identify topics within large, unstructured document collections. The applied topic modeling method is Latent Dirichlet Allocation (LDA), using data collected from Twitter through crawling big data using the Twitter API. The results of this research reveal the discourse on sexual violence discussed by citizens with seven topics, namely: 1) Indonesia's sexual violence emergency; 2) Support for the enactment of the Draft Law on the Prevention of Sexual Violence, opposing parties against the Draft Law on the Prevention of Sexual Violence; 3) Pros and cons of Minister of Education and Culture and Research and Technology Regulation No. 30 of 2021 concerning the Prevention and Handling of Sexual Violence in Higher Education; 4) Sexual violence as sadistic behavior; 5) Sexual violence on campuses and in Islamic boarding schools; 6) Support for Minister of Education and Culture and Research and Technology Regulation No. 30 of 2021 for the prevention and handling of sexual violence on campuses; and 7) Stop sexual violence against children and women. From the analysis of the topic modeling results, it is evident that with a good understanding of citizen engagement facilitated by technology, the younger generation can develop digital citizenship in the practice of online civic engagement.
SLAM algorithm based on improved semantic detection

In the realm of autonomous cars, simulation localization and map creation (SLAM) is a popular topic for study. Traditional SLAM algorithms do not take into account dynamic objects in the environment, which leads to poor pose estimation and other issues. This research suggests a deep learning-based SLAM strategy to address the issue of erroneous feature point extraction in dynamic environments. First, the adaptive threshold approach is used to extract the feature points in accordance with the uniform distribution of feature points in the picture pyramid. Second, an enhanced Mask R-CNN network is built to address the issue of incomplete mask segmentation of dynamic objects. The integrity of the mask edge is strengthened by including the edge detection end in the Mask R-CNN network, and the theoretical and technological groundwork for the virtual production of interior scenes is established. On the TUM RGB-D dataset, this scheme's efficacy was assessed. The findings demonstrate that, in terms of accuracy and resilience in a dynamic environment, the method in this study performs noticeably better than ORB-SLAM2.
COMBINING ESTIMATES OF CONDITIONAL TREATMENT EFFECTS

Estimating a treatment’s effect on an outcome conditional on covariates is a primary goal of many empirical investigations. Accurate estimation of the treatment effect given covariates can enable the optimal treatment to be applied to each unit or guide the deployment of limited treatment resources for maximum program benefit. Applications of conditional treatment effect estimation are found in direct marketing, economic policy, and personalized medicine. When estimating conditional treatment effects, the typical practice is to select a statistical model or procedure based on sample data. However, combining estimates from the candidate procedures often provides a more accurate estimate than the selection of a single procedure. This article proposes a method of model combination that targets accurate estimation of the treatment effect conditional on covariates. We provide a risk bound for the resulting estimator under squared error loss and illustrate the method using data from a labor skills training program.
Image Semantic Segmentation Used in Automatics

This paper presents the importance of segmentation methods used in the object analysis in computer vision and image proccessing field, in the automated processes. Image segmentation is a crucial stage of image processing in the context of industrial automation. The detection of objects is one of the main purposes of image segmentation, and in order to do this, it is necessary to perform the segmentation and extraction of the image's significant elements in advance. Putting pixels into semantic classifications is the foundation of semantic segmentation, this being a deep learning method. In order to apply segmentation in computer vision of automated processes the first step could be to label the objects and then to train a neural network to apply this labels to all the similar objects detected in similar images. In this paper some images were segmented by using labels and deep learning methods. After the theoretical aspects are presented, then some examples are made in Matlab environment.
Short-term prediction of offshore wind power based on an improved N-BEATSX model

Short-term forecasting of offshore wind power plays an essential role in ensuring the safe and stable operation of the grid. Due to the high randomness and variability of offshore wind power, it is difficulty to acquire good results by traditional trend extrapolation of time series of historical power data. In this regard, a novel N-BEATSX model improved by Copula entropy is proposed as a short-term prediction method for offshore wind power. First, the Copula entropy metrics are used to filter predictors to obtaining factors highly relevant to the active power output of offshore wind. Secondly, the auxiliary and historical power data from the wind farms are imported into the N-BEATSX model for future wind power forecasting. Finally, a numerical example is given to demonstrate the feasibility of the developed method. Comparing the prediction results of the proposed model with those of other models, it is demonstrated that the proposed method improves the accuracy of short-term prediction of offshore wind power.
Characterization of atrial and ventricular remodeling in an improved minimally invasive mouse model of transverse aortic constriction

Introduction: Heart failure (HF) is the leading cause of death worldwide. Most large and small animal disease models of HF are based on surgical procedures. A common surgical technique to induce HF is transverse aortic constriction (TAC), which induces pressure overload. The conventional TAC (cTAC) procedure is a highly invasive surgery that is associated with severe inflammation and excessive perioperative deaths. Aim: To establish an improved, minimally invasive TAC (mTAC) procedure that does not require thoracotomy. Methods and results: Following anesthesia, mice were intubated, and a small incision was made at the neck and chest. After cutting the sternum about 4 mm, the aortic arch was approached without opening the pleural cavity. A suture was placed between the brachiocephalic artery and the left common carotid artery. This model was associated with low perioperative mortality and a highly reproducible constriction evidenced by an increased right-to-left carotid blood flow velocity ratio in mTAC mice (5.9 ± 0.2) vs. sham controls (1.2 ± 0.1; P < 0.001). mTAC mice exhibited progressive cardiac remodeling during the 8 weeks post-TAC, resulting in reduced left ventricular (LV) contractility, increased LV end-systolic diameter, left atrial enlargement and diastolic dysfunction, and an increased heart weight to tibia length ratio (mTAC: 15.0 ± 0.8 vs. sham: 10.1 ± 0.6; P < 0.01). Conclusion: Our data show that the mTAC procedure yields a highly reproducible phenotype consisting of LV contractile dysfunction and enlargement, combined with left atrial enlargement and diastolic dysfunction. Potential impact of the findings: This model may be used to test the molecular mechanisms underlying atrial remodeling associated with HF development or to evaluate therapeutic strategies to treat these conditions.
Chemical properties of soils submitted to the application of a bioactivator and basalt and serpentinite powders

ABSTRACT Dependence on external raw materials, the high cost of fertilizers, and the search for more sustainable alternatives have encouraged research with remineralizers. This study aimed to evaluate the effect of adding remineralizers associated or not with a bioactivator on pH, H+Al, Al, P, and K in two soils after four incubation times. A completely randomized experimental design was used in a 5 x 2 x 2 x 2 factorial scheme, as follows: doses of remineralizers (0, 2, 4, 8, and 16 Mg ha-1), bioactivator (with and without application), type of remineralizer (basalt and serpentinite), and soil class (Oxisol and Entisol), with four replications. The soils were incubated for 30, 90, 120, and 240 days after the treatment applications. The samples were submitted to pH, Al, H+Al, P, and K analyses at the end of each incubation period. The use of increasing doses of remineralizers favored an increase in pH, P, and K and a reduction in Al and H+Al in the studied soils. Serpentinite powder was more efficient in neutralizing soil acidity, while basalt powder was more efficient in providing P and K to soils. The bioactivator reduced the reaction time of the remineralizers and favored the increase in K availability.
Association between Hyperuricemia and Hearing Impairment: Results from the Korean National Health and Nutrition Examination Survey

Background and Objectives: Hyperuricemia is associated with a variety of comorbidities. The objective of this study was to investigate the association between hyperuricemia and hearing impairment in Korean adults. Materials and Methods: Audiometric and laboratory test data from the 2019 to 2020 Korean National Health and Nutrition Examination Survey (KNHANES) were used for analysis. Hearing impairment was defined as a pure-tone average (0.5, 1, 2, 4 kHz) threshold level ≥ 41 decibels. The definition of hyperuricemia was different for males and females: >7 mg/dL for males vs. >6 mg/dL for females. Results: A total of 4857 (weight n = 17,990,725) subjects were analyzed. The mean age was 56.8 years old. The weighted prevalence was 12.1% for hyperuricemia and 2.5% for gout. The prevalence of hearing impairment was 13.4%. In the univariable analysis, hyperuricemia was significantly associated with hearing impairment. However, the diagnosis of gout was not associated with hearing impairment. In the multivariable analysis, hyperuricemia (odds ratios (OR): 1.41, 95% confidence interval [CI]: 1.03–1.92, p = 0.030) was associated with hearing impairment along with age (OR: 1.12, 95% CI: 1.10–1.14, p < 0.001), female sex (OR: 0.43, 95% CI: 0.34–0.64, p < 0.001), education (OR: 0.43, 95% CI: 0.30–0.63, p = 0.001), and occupational noise exposure (OR: 1.67, 95% CI: 1.25–2.22, p = 0.001). In the subgroup analysis, hyperuricemia was associated with hearing impairment in females (OR: 1.59, 95% CI: 1.02–2.48, p = 0.041) and the elderly aged 60 years or more (OR: 1.45, 95% CI: 1.05–1.99, p = 0.023). Conclusions: Hyperuricemia was independently associated with hearing impairment, especially in females and the elderly aged 60 years or more.
PASTA: Programming and Automation Support for Scalable Task-Parallel HLS Programs on Modern Multi-Die FPGAs

In recent years, there has been increasing adoption of FPGAs in datacenters as hardware accelerators, where a large population of end users are software developers. While high-level synthesis (HLS) facilitates software programming, it is still challenging to scale large accelerator designs on modern datacenter FPGAs that often consist of multiple dies and memory banks. More specifically, routing congestion and extra delays on these multi-die FPGAs often cause timing closure issues and severe frequency degradation at the physical design level, which are difficult to digest and optimize for high-level programmers using HLS. One promising approach to mitigate such issues is to develop a high-level task-parallel programming model with HLS and physical design co-optimization. Unfortunately, existing studies only support a programming model where tasks communicate with each other via FIFOs, while many applications are not streaming friendly and many existing accelerator designs heavily rely on buffer based communication between tasks. In this paper, we take a step further to support a task-parallel programming model where tasks can communicate via both FIFOs and buffers. To achieve this goal, we design and implement the PASTA framework, which takes a large task-parallel HLS design as input and automatically generates a high-frequency FPGA accelerator via HLS and physical design co-optimization. First, we design a decoupled latency-insensitive buffer channel that supports memory partitioning and ping-pong buffering, which is compatible with the vendor Vitis HLS compiler. In the frontend, we develop an easy-to-use programming interface to allow end users to use our buffer channel in their applications. In the backend, we provide automatic coarse-grained floorplanning and pipelining for designs that use our proposed buffer channel. We test PASTA on a set of task-parallel HLS designs that use buffers for task communication and show an average of 36% (up to 54%) frequency improvement for large design configurations.
AutoRooms: Automatic Room Segmentation Based on Wall Constraints from Point Clouds

How to overcome the noise interference between rooms and obtain a complete and accurate segmentation result is not only a research hotspot, but also a necessity for indoor navigation and 3D reconstruction. Walls are basic components of a building, and are also natural boundaries between rooms. From this perspective, this paper proposes a fully automatic room segmentation approach based on wall constraints using point clouds. Firstly, wall centerlines are extracted as boundaries between rooms to cut off their connection. Then, missing walls are inferenced based on the Manhattan world assumption to obtain a relatively accurate initial segmentation of point clouds. Finally, a global segmentation optimization is conducted by formulating room segmentation as a Markov Random Field optimization problem, which is efficiently solved by the graph cut algorithm. Our method is compared with the state-of-the-art methods on multiple datasets which have various point densities and noise levels. Experiments show that our method significantly outperforms the-state-of-art methods in terms of precision, recall and IoU.
Water Level in Observation Wells Simulated From Fracture and Matrix Water Heads Outputted by Dual‐Continuum Hydrogeological Models: POWeR‐FADS

Do observation wells in fractured porous aquifers measure water head in the fracture network, water head in the matrix, or some combination of both? This question necessarily arises when calibrating dual‐continuum hydrogeological models against on‐field data. One can assume that observation wells measure fracture water head, because matrix permeability is negligible compared to fracture permeability. Nevertheless, this reasoning is invalid for wells poorly‐connected to the fractures. Yet, the possibility of such a poor connection at given depths has never been implemented in a physics‐based manner when comparing matrix and fracture water heads simulated by dual‐continuum models to on‐field data. To fill this knowledge gap, a physically based, easy to calibrate, open‐source postprocessing tool, POWeR‐FADS (Program for Observation Well Representation in Fractured Aquifer Dual‐continuum Simulations), available at https://github.com/BJeannot1/POWeR-FADS, has been developed. It introduces as parameters well geometry and the altitude of lowest interception of the fractures by the well. From these, POWeR‐FADS nonintrusively postprocesses time series of matrix and fracture water heads at the well, as simulated by any planar, bidimensional dual‐continuum hydrogeological model, to calculate water exchanges involving the observation well and thus the evolution of water level in the well. Synthetic test cases show that POWeR‐FADS makes it possible to simulate peculiar behaviors that are similar to patterns actually observed by the authors in on‐site observation wells of a fractured porous aquifer, like “floors” in observed water levels, delayed but sharp rises at the beginning of recharge events, or inflexion points accelerating the drawdown velocity during the recession phase.
Understanding Factors Associated with Motivation to Quit Vaping among Vapers in the Federal Territory of Kuala Lumpur, Malaysia

The prevalence of vaping worldwide is showing an upward trend. This study aimed to determine the factors associated with motivation to quit vaping among vapers in the Federal Territory of Kuala Lumpur, Malaysia, through a cross-sectional, purposive sampling study. Respondents were required to complete a questionnaire consisting of vapers’ sociodemographic questions, habitual behavioral pattern questions, the e-Fagerström Test of Nicotine Dependence, the Glover–Nilsson Smoking Behavioral Dependence Questionnaire, perception questions, motivation to quit questions, and withdrawal symptom questions. A total of 311 vapers participated in this study. The majority of the vapers were male (84.6%), younger (18–25 years) (55.3%), and with monthly income less than RM 4000 (USD 868; 83.9%). The level of motivation to quit vaping was found to have a significant association with the perception of vaping being as satisfying as cigarette smoking (p = 0.006) and mild to very strong nicotine dependence (p = 0.001). Participants who recorded moderate and strong habitual vaping behaviors had lower odds of having high motivation to quit vaping compared to those recording slight habitual behaviors (OR = 0.279, 95%CI(0.110–0.708), p = 0.007 and OR = 0.185, 95%CI(0.052–0.654), p = 0.009, respectively). Factors associated with higher motivation to quit vaping could be explored to gain better understanding of how to increase their motivation level for future quit attempts.
Nested case‐control study designs for left‐truncated survival data

The determination of risk factors for disease incidence has been the subject of much epidemiologic research. With this goal a common study design entails the follow‐up of an initially disease‐free cohort, keeping track of the dates of disease incidence (onset) and ascertaining covariate (putative risk factor) information on the full cohort. However, the collection of certain covariate information on all study subjects may be prohibitively expensive and, therefore, the nested case‐control study has commonly been used. The high cost of full covariate information on all subjects also arises when determining risk factors for “failure,” death say, “following” disease onset, in particular, in a prevalent cohort study with follow‐up; in such a study a cohort of subjects with existing disease is followed. We here adapt nested case‐control designs to the setting of a prevalent cohort study with follow‐up, a topic previously not addressed in the literature. We provide the partial likelihood under risk set sampling and state the asymptotic properties of the estimated covariate effects and baseline cumulative hazard. We address the following design questions in the context of prevalent cohort studies with follow‐up: How many subjects should be included in the sampled risk sets for efficient estimation? In what way is the proportion of censored subjects associated with the benefit of a nested case‐control design? What proportion of overall variance is attributable to risk set sampling? This work is motivated by the anticipated analysis of data on survival with Parkinson's Disease, being collected as part of the ongoing Canadian Longitudinal Study on Aging. The Canadian Journal of Statistics 45: 4–28; 2017 © 2017 Statistical Society of Canada
Quantum Versus Classical Nature of the Early Universe and Its Consequences (Entanglement

We look at early universe space-time which is characterized by a transition from Pre Planckian to Planckian space-time. In doing so we also invoke the geometry of Octonionic non commutative structure and when it breaks down as a trigger point for the release of entropy. Following Lloyd, as to entropy production with 1 count of entropy linked to one graviton via Ng Infinite quantum statistics, and we follow up on this modeling of entropy production by examining if we can relate the presumed graviton production with the behavior of Planck sized mini black holes, in the early phases of cosmological expansion. This analysis is in tandem with considerations as to what is essential as to accessing the quantum versus classical nature of the early universe, with the above listed as consequences. We close with the 2nd half of our work which is reviewing relic gravitons, and their role in presumed entanglement from a prior universe construction to the present universe, possibly for the unification of physical law, from cycle to cycle.
A Low-Profile Dual-Band Antenna With Even- and Odd-Mode Resonances for 5G Mobile Applications

In this paper, a low-profile dual-band antenna integrated with the back cover of the mobile device is presented for fifth-generation (5G) terminal applications. The proposed antenna element is composed of two pairs of perpendicular metal strips, a coupling feeding probe, and a parasitic shorted patch. By using the proposed feeding structure, the even and odd modes can be excited on the metal strips simultaneously, which can cover two working bands. Meanwhile, to further improve the working bandwidth, a parasitic resonance on the shorted patch is also utilized. Finally, a four-element multiple-input-multiple-output (MIMO) antenna is designed and discussed. As a result, the proposed MIMO antenna can cover 3.40-4.02 GHz and 4.75-5.00 GHz. The isolation is higher than 15 and 21 dB in the lower and higher bands. And the total efficiency is also higher than 48% in the working bands, while the calculated envelope correlation coefficients (ECCs) are lower than 0.15.
Some Comparison of Solutions by Different Numerical Techniques on Mathematical Biology Problem

We try to compare the solutions by some numerical techniques when we apply the methods on some mathematical biology problems. The Runge-Kutta-Fehlberg (RKF) method is a promising method to give an approximate solution of nonlinear ordinary differential equation systems, such as a model for insect population, one-species Lotka-Volterra model. The technique is described and illustrated by numerical examples. We modify the population models by taking the Holling type III functional response and intraspecific competition term and hence we solve it by this numerical technique and show that RKF method gives good results. We try to compare this method with the Laplace Adomian Decomposition Method (LADM) and with the exact solutions.