Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic Literature Review: Research on Martial Arts Competition Using Vos Viewers in the 2018-2022 Google Scholar Database

Martial arts competition is a sport of self-defense skills that employs the ability to defend oneself, repel attacks and ultimately attack enemies, with or without weapons. This study uses a descriptive quantitative method with a bibliometrics approach. The research data used for scientific journals or articles spanning 2018-2022 is sourced from the Google Scholar database. A search for scientific articles was carried out using the Publish or Perish application with the keyword "Martial arts competition" in the article title, category, abstract, key words. The map for the development of the Martial arts competition scientific publication model was analyzed using the VOSViewer application. From the search results using the Publish or Perish application, the maximum number of searches used is 1000 articles, limited to 100 article publications according to the keywords entered, then by type filtering using articles there are 100 papers written in article form. Based on the results and discussion, it can be concluded that there has been a decline every year. In 2018 there were 23% who researched martial arts competition, while in 2019, 2020 and 2021 in the 20% category then in 2022 it will decrease to 17%. on the publication of articles with the keywords Martial Arts Competition in the 2018-2022 period on the Google scholar database
Development, Feasibility, Impact and Acceptability of a Community Pharmacy-Based Diabetes Care Plan in a Low–Middle-Income Country

Informed by existing research, mostly from high-income countries, this study aimed to develop and test the feasibility of a community pharmacy person-centred goal-setting intervention for people living with type 2 diabetes in a low–middle-income country—Nigeria. The Medical Research Council (MRC) guidance for developing complex interventions framed the intervention development. Patients participated in monthly community pharmacist consultations over six months. Self-reported and clinical outcome measures were collected at baseline and study completion and analysed in STATA V.14. Twenty pharmacists in 20 pharmacies completed the research and enrolled 104 patients. Of these, 89 patients had complete study data, and 70 patients also completed a post-study evaluation questionnaire. In addition, 15 patients and 10 pharmacists were interviewed. All outcome measures showed statistically significant improvements (p < 0.05). Clinical outcomes (BMI, waist circumference, and fasting plasma glucose) improved significantly. Mean patient activation measure (PAM©), quality of life (EQ-VAS©), and medication adherence improved from baseline to study completion. Eighty-eight per cent of questionnaire respondents were satisfied with the service. Interviews indicated care plan acceptability, patient satisfaction, empowerment, and service enthusiasm. Identified barriers to the consultations included time and technology. This study developed a feasible, effective, well-perceived community pharmacy diabetes care plan in Nigeria.
Improving door-to-reperfusion time in acute ischemic stroke during the COVID-19 pandemic: experience from a public comprehensive stroke center in Brazil

Background The global COVID-19 pandemic has had a devastating effect on global health, resulting in a strain on healthcare services worldwide. The faster a patient with acute ischemic stroke (AIS) receives reperfusion treatment, the greater the odds of a good functional outcome. To maintain the time-dependent processes in acute stroke care, strategies to reorganize infrastructure and optimize human and medical resources were needed. Methods Data from AIS patients who received thrombolytic therapy were prospectively assessed in the emergency department (ED) of Hospital de Clínicas de Porto Alegre from 2019 to 2021. Treatment times for each stage were measured, and the reasons for a delay in receiving thrombolytic therapy were evaluated. Results A total of 256 patients received thrombolytic therapy during this period. Patients who arrived by the emergency medical service (EMS) had a lower median door-to-needle time (DNT). In the multivariable analysis, the independent predictors of DNT >60 min were previous atrial fibrillation (OR 7) and receiving thrombolysis in the ED (OR 9). The majority of patients had more than one reason for treatment delay. The main reasons were as follows: delay in starting the CT scan, delay in the decision-making process after the CT scan, and delay in reducing blood pressure. Several actions were implemented during the study period. The most important factor that contributed to a decrease in DNT was starting the bolus and continuous infusion of tPA on the CT scan table (decreased the median DNT from 74 to 52, DNT ≤ 60 min in 67% of patients treated at radiology service vs. 24% of patients treated in the ED). The DNT decreased from 78 min to 66 min in 2020 and 57 min in 2021 (p = 0.01). Conclusion Acute stroke care continued to be a priority despite the COVID-19 pandemic. The implementation of a thrombolytic bolus and the start of continuous infusion on the CT scan table was the main factor that contributed to the reduction of DNT. Continuous monitoring of service times is essential for improving the quality of the stroke center and achieving better functional outcomes for patients.
Analysis of a Spatially-distributed Wilson-Cowan Model of Cortex

The Wilson-Cowan equations represent a model for the mean activities of localized excitatory and inhibitory populations in sensory cortex. In this document, we extend this model to include spatially-distributed connections in a 1D continuum model to study spatiotemporal patterns, such as traveling waves and doubly periodic patterns. We use bifurcation theory and continuation methods to understand how these organized patterns of activity arise in the network. In addition, we often simulate a (spatial) discretization of the network (to approximate the continuum) and compare these with our analytical theory to give evidence as to how these patterns may become unstable. In the later chapters, we make comparisons with a nonsmooth version of the model to understand the consequences of this approximation.
Estimation of soil losses due to water erosion in the Dagua River Basin, Colombia

The Dagua river basin, in Colombia, is the most important source of water for the Valle del Cauca ecosystem, however, due to poor agricultural practices, it has been affected by water erosion. This study aimed at estimating soil erosion in the Dagua river basin, using the universal soil loss equation (USLE). The results show that most of the area presents erosivities that are between 1000-5000 MJ.mm. ha-1, corresponding to low and very low categories. On the other hand, erodibility ranged from 0.143 to 0.842 t. ha.h. MJ-1 mm-1 ha-1, which is framed in the categories from weak to extremely erodable, where the low to medium category predominates. Regarding soil losses due to erosion, it was found that more than 20% of each of the municipalities of Dagua, Restrepo, La Cumbre, and Vijes, showed high and very high erosion, particularly in the areas with bare soils and crops such as pineapple, contribute strongly, sometimes reaching over 1000 t ha-1 yr-1. Therefore, it is important to promote practices such as contour or contour planting, integrated crop cover management, land uses that integrate trees, and in more critical cases to consider ecological restoration processes.
Sleep-Wake and Body Position Classification with Deep Learning using Pressure Sensor Mat Measurements

Polysomnography, the gold standard of sleep measurement, is a time-intensive, costly, and rather invasive procedure. Using under-bed pressure sensor arrays data simultaneously recorded with standard polysomnography, this study demonstrates that deep learning can be used to classify body position and differentiate sleep from wake. All measurements were performed in people with suspected sleep disorders referred for clinical assessments at a sleep laboratory. To perform the classification tasks, we used supervised learning and temporal convolution networks. Performance was assessed with leave-one-out cross validation on 84 participants for body position classification and 70 participants for sleep-wake classification. Results demonstrate that a pressure sensor array placed under the mattress with less than 100 sensors can outperform previous sleep-wake detection methods and is competitive with previous methods for body position classification. Our pressure sensor arrays differ from the mats used in previous work as they use significantly less sensors and are located under the mattress, making them less obtrusive. This tool has great potential as a cost-efficient mean of assessing sleep while reducing patient burden and the workload of specialized staff.
Collaborative Writing as Bio‐Digital Quilting: A Relational, Feminist Practice Towards “Academia Otherwise”

In this article, we explore how quilted poetry as methodology, through the practice of collaborative writing, can help us to attune to and think with what is un/seen, un/heard, and un/spoken in our bio‐digital ways of working, as a way of resisting normative, exploitative practices in the neoliberal academia. We are a group of academics with different journeys and localities, connected by a common interest in the effects of boundaries, the dynamics of power, and the desire to do things differently. Drawing on our daily mundane encounters with/in both virtual and physical spaces of academia, including Teams meetings, Outlook emails, Google documents, and Miro board collaborations, we write quilted poetry with fragments of precarious matter: silences, messages, rhythms, feelings, and materialities. We attend to the entanglement of our bodies and their enmeshment in technology and share how bringing relational, feminist theories and the bio‐digital together has helped us to both materialise new patterns of relations and enact a more ethical approach to working in academia.
Control of Discrete-Time Stochastic Systems With Packet Loss by Event-Triggered Approach

Event-triggered schemes are characterized in less communication traffic while maintaining the resulting controlled plant’s desired stability and performance criteria. In the presence of packet dropouts, this paper is concerned with the modeling and control problems for a class of discrete-time stochastic systems with event-triggered schemes. Two different mathematical analysis methods are proposed to model the packet loss when an event-triggered scheme is subject to packet loss. First, a stochastic distributed sequence satisfying the Bernoulli process is utilized to model the triggered packets transmitted in the communication networks. Considering the difference between time-triggered and event-triggered schemes, an equivalent model with a random sequence not satisfying a Bernoulli distribution process is also analyzed, which is not the same as some existing results in the literature. Then, the mean-square exponential stability of resulting augmented system is guaranteed and the prescribed <inline-formula> <tex-math notation="LaTeX">${H}_{\infty }$ </tex-math></inline-formula> performance level is achieved by solving resulting discrete <inline-formula> <tex-math notation="LaTeX">${P}$ </tex-math></inline-formula>-problem. Two examples with simulations are provided to validate the analytical results and demonstrate the effectiveness of the proposed co-design techniques.
Power Spectral Analysis of Short-Term Heart Rate Variability in Healthy and Arrhythmia Subjects by the Adaptive Continuous Morlet Wavelet Transform

Power spectral analysis of short-term heart rate variability (HRV) can provide instant valuable information to understand the functioning of autonomic control over the cardiovascular system. In this study, an adaptive continuous Morlet wavelet transform (ACMWT) method has been used to describe the time-frequency characteristics of the HRV using band power spectra and the median value of interquartile range. Adaptation of the method was based on the measurement of maximum energy concentration. The ACMWT has been validated on synthetic signals (i.e. stationary, non-stationary as slow varying and fast changing frequency with time) modeled as closest to dynamic changes in HRV signals. This method has been also tested in the presence of additive white Gaussian noise (AWGN) to show its robustness towards the noise. From the results of testing on synthetic signals, the ACMWT was found to be an enhanced energy concentration estimator for assessment of power spectral of short-term HRV time series compared to adaptive Stockwell transform (AST), adaptive modified Stockwell transform (AMST), standard continuous Morlet wavelet transform (CMWT) and Stockwell transform (ST) estimators at statistical significance level of 5%. Further, the ACMWT was applied to real HRV data from Fantasia and MIT-BIH databases, grouped as healthy young group (HYG), healthy elderly group (HEG), arrhythmia controlled medication group (ARCMG), and supraventricular tachycardia group (SVTG) subjects. The global results demonstrate that spectral indices of low frequency power (LFp) and high frequency power (HFp) of HRV were decreased in HEG compared to HYG subjects (p<0.0001). While LFp and HFp indices were increased in ARCMG compared to HEG (p<0.00001). The LFp and HFp components of HRV obtained from SVTG were reduced compared to other group subjects (p<0.00001).
Nanobody-Based EGFR-Targeting Immunotoxins for Colorectal Cancer Treatment

Immunotoxins (ITXs) are chimeric molecules that combine the specificity of a targeting domain, usually derived from an antibody, and the cytotoxic potency of a toxin, leading to the selective death of tumor cells. However, several issues must be addressed and optimized in order to use ITXs as therapeutic tools, such as the selection of a suitable tumor-associated antigen (TAA), high tumor penetration and retention, low kidney elimination, or low immunogenicity of foreign proteins. To this end, we produced and characterized several ITX designs, using a nanobody against EGFR (VHH 7D12) as the targeting domain. First, we generated a nanoITX, combining VHH 7D12 and the fungal ribotoxin α-sarcin (αS) as the toxic moiety (VHHEGFRαS). Then, we incorporated a trimerization domain (TIEXVIII) into the construct, obtaining a trimeric nanoITX (TriVHHEGFRαS). Finally, we designed and characterized a bispecific ITX, combining the VHH 7D12 and the scFv against GPA33 as targeting domains, and a deimmunized (DI) variant of α-sarcin (BsITXαSDI). The results confirm the therapeutic potential of α-sarcin-based nanoITXs. The incorporation of nanobodies as target domains improves their therapeutic use due to their lower molecular size and binding features. The enhanced avidity and toxic load in the trimeric nanoITX and the combination of two different target domains in the bispecific nanoITX allow for increased antitumor effectiveness.
Thomson scattering laser-electron X-ray source for reduction of patient radiation dose in interventional coronary angiography

It was medical applications that stimulated F. Carrol in the early 1990s to start the research of on relativistic Thomson scattering X-ray sources, as a part of the infrastructure of the future society. The possibility to use such a source in interventional cardiology is discussed in this paper. The replacement of X-ray tube by relativistic Thomson scattering Xray source is predicted to lower the patient radiation dose by a factor of 3 while image quality remains the same. The required general characteristics of accelerator and laser units are found. They can be reached by existing technology. A semiempirical method for simulation of medical and technical parameters of interventional coronary angiography systems is suggested.
Time integration for extended discontinuous Galerkin methods with moving domains

We study time integration schemes for discontinuous Galerkin discretizations of problems with moving immersed interfaces. Two approaches have been discussed in literature so far: splitting schemes and space‐time methods. Splitting schemes are cheap and easy to implement, but are non‐conservative, inherently limited to low orders of accuracy, and require extremely small time steps. Space‐time methods, on the other hand, are conservative, allow for large time steps, and generalize to arbitrary orders of accuracy. However, these advantages come at the expense of a severe growth the systems to be solved. Within this work, we present a generic strategy that combines the advantages of both concepts by evaluating numerical fluxes in a moving reference frame and by making use of a conservative cell‐agglomeration strategy. We study the performance of this strategy in combination with backward‐difference‐formulas and explicit Runge‐Kutta schemes in the context of the scalar transport equation, the Burgers equation, and the heat equation. Our results indicate that higher order spatial and temporal convergence rates can be achieved without increasing the size of the systems to be solved.
Political Preferences and the Spatial Distribution of Infrastructure: Evidence from California's High-Speed Rail

How do political preferences shape transportation policy? We study this question in the context of California’s High-Speed Rail (CHSR). Combining geographic data on votes in a referendum on the CHSR with a model of its expected economic benefits, we estimate the weight of economic and non-economic considerations in voters’ preferences. Then, comparing the proposed distribution of CHSR stations with alternative placements, we use a revealed-preference approach to estimate policymakers’ preferences for redistribution and popular approval. While voters did respond to expected real-income benefits, non-economic factors were a more important driver of the spatial distribution of voters’ preferences for the CHSR. While the voter-approved CHSR would have led to modest income gains, proposals with net income losses also would have been approved due to political preferences. For the planner, we identify strong preferences for popular approval. A politically-blind planner would have placed the stations closer to dense metro areas in California. ∗We thank Andrii Parkhomenko and Jonathan Dingel for their conference discussions, and seminar participants at ASSA Meetings, Berkeley, Chicago, CREI, Chicago-Princeton Spatial Economics Conference, Harvard-MIT, Michigan, NBER Transportation, Penn State, SED, Timbergen, Toronto, UCLA ITS, UTDT, and West Coast Spatial Workshop for helpful feedback. We thank the NBER and the US DOT for financial support.
Development of a Laser Induced Fluorescence (LIF) system on the Plasma Material Interaction System (PLAMIS-II) device

A laser induced fluorescence (LIF) system has been developed for the plasma material interaction system (PLAMIS-II) device, which is equipped with a unique plasma gun composed of a LaB6 cathode and two anodes with electromagnets to generate a focused dense plasma. PLAMIS-II simulates the interactions of plasma with different materials and is to be used for the test of plasma facing components of fusion devices. The LIF system is composed of a seed laser with Littmann/Metcalf cavity and a master oscillator power amplifier to pump 3d4F7/2 metastable argon ion to 4p4D5/2 level at the wavelength of 668.61 nm, which has the following input parameters: laser power = 20 mW, line width < 100 kHz, and a mode-hop free tuning range > 70 GHz. For in-situ measurement of laser wavelength, the wavelength spectrum of an iodine cell was measured by a photo-transistor during LIF measurement. To measure argon ion temperature (Ti) and drift velocity (vd) in PLAMIS-II, the fluorescence light with the wavelength of 442.72 nm, emitted from 4p4D5/2 level to 4s4P3/2 level and passing through 1 nm band-width filter, was collected by the photomultiplier tube combined with a lock-in amplifier and a chopper with frequency of 3 kHz. Initial data of Ti and vd were analysed in terms of gas flow rate and applied power.
LPCUNet:A Lightweight Pure CNN UNet for Efficient Urban Scene Remote Sensing Semantic Segmentation

Semantic segmentation, a fundamental task in computer vision, has developed rapidly in recent years. Semantic segmentation of remote sensing urban scene images, utilized in tasks such as land cover mapping, urban change detection, environmental preservation, and economic assessment, has also received much attention. In order to extract global semantic features, recent research has focused on combining transformers with CNN, which have great potential in global information modeling, for semantic segmentation models. However, the hybrid method of CNN and Vision Transformer still suffers from low latency. And the MLP of the visual transformer results in many parameters. To address these issues, we propose a lightweight pure CNN UNet(LPCUNet) model, which introduces a sizeable convolutional kernel to capture the global context. Moreover, a simple fusion module dynamically fuses local and global features. Extensive experiments show that our proposed method achieves state-of-the-art performance while faster latency. More precisely, the LPCUNet model demonstrated impressive performance with 83.3% and 86.4% mean Intersection over Union (mIoU) scores on the Vaihingen and Potsdam datasets respectively.
Broadband SLMBA Design Including 90° Hybrid Frequency Response Using an Automatic Methodology

This article proposes an automatic methodology for the design of a sequential load-modulated balanced amplifier (SLMBA), which takes corrections for its output combiner’s (a 90° hybrid) frequency response. The method uses a properly selected control amplifier phase to compensate for the 90° hybrid frequency response and finds the accurate impedances that are observed towards the hybrid. Based on this, and on a multi-dimensional search performed over the control and balanced devices’ load-pull data, the optimum termination impedance profiles are found, considering the desired output power levels and efficiency. The balanced power amplifiers off-state impedance is also incorporated in the methodology to prevent the otherwise unavoidable SLMBA performance degradation. Consequently, three scattering parameter matrices are obtained in a polynomial form, corresponding to the output matching networks of the balanced and control amplifiers, and the required post-matching network at the $50~\Omega $ output port. Finally, the method is validated with a practical SLMBA implementation, synthesizing real matching networks that realize the derived S-parameter matrices.