Miracle-dz/UniFAR-FTUs huggingface dataset:
Solving Ordinary Differential Equations I: Nonstiff Problems

This book deals with methods for solving nonstiff ordinary differential equations. The first chapter describes the historical development of the classical theory, and the second chapter includes a modern treatment of Runge-Kutta and extrapolation methods. Chapter three begins with the classical theory of multistep methods, and concludes with the theory of general linear methods. The reader will benefit from many illustrations, a historical and didactic approach, and computer programs which help him/her learn to solve all kinds of ordinary differential equations. This new edition has been rewritten and new material has been included.
PropaLTL at DIPROMATS: Incorporating Contextual Features with BERT's Auxiliary Input for Propaganda Detection on Tweets

In this paper, we describe our participation in the Automatic Detection and Characterization of Propaganda Techniques from Diplomats shared task (DIPROMATS). For this, we studied the inclusion of features grounded on contextual information. That is, our hypothesis was inspired in combining the text of tweets with contextual attributes such as its geographical origin, type of message, and emotions, aiming to improve the performance of the detection of propagandistic tweets. Our strategy of leveraging context data obtained very competitive scores in the binary propaganda identification task : the top position for Spanish out of 18 runs, and the second position for English out of 30 runs.
DR 2 : Disentangled Recurrent Representation Learning for Data-efficient Speech Video Synthesis

Although substantial progress has been made in audio-driven talking video synthesis, there still remain two major difficulties: existing works 1) need a long sequence of training dataset ( > 1h) to synthesize co-speech gestures, which causes a significant limitation on their applicability; 2) usually fail to generate long sequences, or can only generate long sequences without enough diversity. To solve these challenges, we propose a Disentangled Recurrent Representation Learning framework to synthesize long diversified gesture sequences with a short training video of around 2 minutes. In our framework, we first make a disentangled latent space assumption to encourage unpaired audio and pose combinations, which results in diverse “one-to-many” mappings in pose generation. Next, we apply a recurrent inference module to feed back the last generation as initial guidance to the next phase, enhancing the long-term video generation of full continuity and diversity. Comprehensive experimental results verify that our model can generate realistic synchronized full-body talking videos with training data efficiency.
SPARQL with Property Paths (version with appendix)

The original SPARQL proposal was often criticized for its inability to navigate through the structure of RDF documents. For this reason property paths were introduced in SPARQL 1.1, but up to date there are no theoretical studies examining how their addition to the language aﬀects main computational tasks such as query evaluation, query containment, and query subsumption. In this paper we tackle all of these problems and show that although the addition of property paths has no impact on query evaluation, they do make the containment and sub-sumption problems substantially more diﬃcult.
Examination of constituents of coconut natural product for dietary and therapeutic purposes

Abstract Analysis of constituents of coconut fruit for nutritional and medicinal purposes for people from diverse culture was done to establish the scientific claims for its diverse medicinal applications. The concentrations of glucose, calcium, potassium, sodium, iron, zinc along with that of vitamins C and E in the coconut milk and oil were determined using standard laboratory methods, while pH and specific gravity of coconut milk and oil were also estimated. The mean concentrations of constituents of coconut milk were 2645.7 ± 275.3 mg/L for glucose, 0.46 ± 0.06 mg/L for vitamin C, 0.14 ± 0.02 mg/L for vitamin E, 2130.6 ± 144.4 mg/L for calcium, 24.56 ± 2.61 mg/L for iron, 7.06 ± 0.88 mg/L for zinc, 1127 ± 105.7 Meq/L for potassium and 20.22 ± 2.46 Meq/L for sodium, respectively. Coconut oil contained 347.4 52.9 mg/L of glucose, 0.11 ± 0.02mg/L of vitamin C, 0.05 ± 0.004 mg/L of vitamin E, 102.6 ± 18.2 mg/L of calcium, 4.53 ± 0.61 mg/L of iron, 1.04 ± 0.15 mg/L of zinc, 222.5 ± 54.0 Meq/L of potassium and 3.01 ± 0.20 Meq/L of sodium, respectively. The difference between the concentrations of nutrients in coconut milk and coconut oil was statistically significant (P < 0.05); coconut milk is a richer source of the aforementioned nutrients than its oil; routine consumption of coconut or its medicinal application especially in medical conditions where some of the aforementioned nutrients are implicated could be considered.
Feasibility of Caregiver - Involvement in the Clinical Monitoring of Newborns and Young Infants

Background: Neonatal and infant deaths contribute substantially to under - 5 mortality in South Africa, with inadequate monitoring and delayed response to signs of illness remaining important modifiable factors. Objectives: This study was undertaken to evaluate the acceptability, feasibility and use of a Family Monitoring booklet among caregivers and healthcare workers and assess the impact on caregivers ’ confidence and knowledge levels. Methods: A mixed methods descriptive study was undertaken in four KwaZulu - Natal hospitals. Participants included mothers of well preterm babies in Kangaroo Mother Care (KMC) units, post - Caesarean section mothers and healthcare workers. Data collection encompassed caregiver knowledge questionnaires, audits of the completed Family Monitoring tools, caregiver focus groups and healthcare worker questionnaires. The descriptive and thematic analysis was completed in Microsoft Excel. Results: Overall, 262 mothers were recruited: 109 in the KMC group and 153 in the postnatal group. The reported acceptability of Family Monitoring was good, with completion rates of 92.2% among mothers and 66.7% among healthcare workers. The mean caregiver knowledge score was 78.0% pre - exposure with a mean percentage increase of 4.3% (p<0.001). Major themes included initial anxiety, ease of use, increased maternal confidence and knowledge and the importance of explanation and peer support for its use. Conclusions : Implementing the Family Monitoring tool was acceptable to mothers and healthcare workers at four Kwa-Zulu - Natal hospitals, with modest but significant increases in the mother ’ s knowledge scores and high completion rates among mothers. Further evaluations of family - centred care interventions are needed in South Africa.
Stochastic integral equations without probability

A pathwise approach to stochastic integral equations is advocated. Linear extended Riemann-Stieltjes integral equations driven by certain stochastic processes are solved. Boundedness of the p-variation for some 0< p <2 is the only condition on the driving stochastic process. Typical examples of such processes are infinite-variance stable L6vy motion, hyperbolic Levy motion, normal inverse Gaussian processes, and fractional Brownian motion. The approach used in the paper is based on a chain rule for the composition of a smooth function and a function of bounded p-variation with 0 < p < 2.
San Joaquin River Water Quality Issues

Our work on Delta water quality issues began in the summer of 1989 as part of reviewing the expected water quality in the Delta Wetlands in-Delta water storage reservoirs. In the spring 1999 we became involved in the San Joaquin River (SJR) Deep Water Ship Channel (DWSC) low-DO problem as a technical resource to the SJR DO TMDL Steering Committee. It was through this activity that we began to become familiar with SJR water quality issues and the impacts of the SJR watershed water diversions and pollutants on Delta water quality. In 2000, with CALFED support, we became the coordinating PIs for a $2-million project designed to investigate the impact of SJR watershed-derived chemicals on the SJR DWSC low-DO problem. This activity resulted in our developing a 2003 Synthesis Report of what was known about the cause of the DWSC low-DO problem and the sources of oxygen demand derived from the SJR DWSC watershed. Since the completion of the Synthesis Report, without support, we have continued to become familiar with CVRWQCB water quality management efforts in the SJR watershed. We have published a series of reports on SJR and Delta water quality issues which are available on our website, www.gfredlee. com, in the Watershed Studies section, San Joaquin River Watershed Delta subsection. The report presented herein contains an Abstract, a Summary of water quality issues and a more detailed discussion of water quality issues, with references to sources of information that provide additional discussions of these issues. It is an integration and synthesis of our reports and other information on SJR water quality issues and the impact of SJR watershed-derived constituents and water diversions on Delta water quality. It also updates the information on Delta water quality that was presented in our 2004 Delta Water Quality Issues report. Appendix B presents a summary of our background and expertise that serve as a technical base for the development of this report. Acknowledgments We wish to thank all of those who took time to discuss with us, write about, organize meetings, and make presentations on SJR water quality issues, which serves as a basis for much of the information presented in this report. A draft of this report on SJR Water Quality Issues was made available to over 120 individuals to provide an opportunity for review and comments. Appropriate changes were included in the final version. This report is planned to be a …
Fast Optimal Antenna Selection for Massive MIMO

This paper investigates the optimal antenna selection problem for very large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard, and solving it optimally requires exponential computation time, which limits its application in very large MIMO systems. In order to develop a feasible optimal method and help evaluate the performance gap of low-complexity algorithms in the scenarios of our interests, we start by proposing a new objective function, which reformulates the original problem into a shortest path finding. By combining the best-first search strategy, we further develop an efficient pruning algorithm, namely BFS-AS, for finding the optimal antenna combination. Our simulation results show that the proposed BFS-AS not only achieves the exact optimal performance, but also has a fixed space complexity and a much lower average time complexity with comparison to the existing optimal performance achieving approaches.
Overcoming In-band Interference in Airborne Ultrasounds: A Robust System Design

This paper introduces a novel system designed to enhance the robustness of airborne ultrasound technology against in-band interference. By integrating MEMS-based transceivers, the system achieves ultrasonic pulse-echo operation up to 100 kHz while maintaining low power consumption. A dedicated hardware readout block effectively addresses the signal distortion and encodes the transmission intervals in the received signal. The experimental results demonstrate the key role of hardware-defined reference points for the start of the frame for accurate echo detection. This optimization ensures superior performance compared to the reference system, especially in the presence of in-band interference. The system’s multi-channel readout feature shows potential for applications like 3D object detection and tracking, even in noise-heavy environments.
Single-channel, ultraportable, real-time imaging system based on deep learning: a proof-of-concept

There is a growing demand for user-friendly technologies that may empower individuals to independently monitor their physiological parameters. Ultrasound systems are highly promising for healthcare self-monitoring. To address these challenges, in this work, we present the proof of concept (PoC) of a real-time ultraportable system with a single-channel transmission and reception strategy and a deep learning-based image reconstruction method. The PoC uses the ULA-OP 256 scanner, employing a monostatic synthetic aperture focusing technique, along with a convolutional neural network (CNN) trained to generate B-mode images. Testing was carried out using a CNN running on a mid-range graphics processing unit (GPU), both on phantoms and in vivo scenarios, comparing the image quality achieved by the CNN with that of a delay-and-sum (DAS) beamformer. The results highlight that the CNN outperformed the DAS, showing a better image quality. Furthermore, the CNN achieved a real-time frame rate of 37.0 frames per second, proving that ultraportable ultrasound systems are highly promising for the future of self-monitoring instruments.
Adaptive Filter for Biosignal-Driven Force Controls Preserves Predictive Powers of sEMG

Electromyographic controls based on machine learning rely on the stability and repeatability of signals related to muscular activity. However, such algorithms are prone to several issues, making them non-viable in certain applications with low tolerances for delays and signal instability, such as exoskeleton control or teleimpedance. These issues can become dramatic whenever, e.g., muscular activity is present not only when the user is trying to move but also for mere gravity compensation, which generally becomes more prominent the more proximal a muscle is. A substantial part of this instability is attributed to electromyography's inherent heteroscedasticity. In this study, we introduce and characterize an adaptive filter for sEMG features in such applications, which automatically adjusts its own cutoff frequency to suit the current movement intention. The adaptive filter is tested offline and online on a regression-based joint torque predictor. Both the offline and the online test show that the adaptive filter leads to more accurate prediction in terms of root mean square error when compared to the unfiltered prediction and higher responsiveness of the signal in terms of lag when compared to the output of a conventional low-pass filter.
Are we Ready to Adopt Facial Recognition Payment System?: The Perspective of Cognitive Appraisal Theory

Facial recognition payment (FRP) is a new-age technology that empowers the cashless society, who may be physical or virtual customers of marketplaces and visitors to countries accepting FRP. Despite the potential of FRP to contribute to sustainable development (e.g., mitigate physical contact and reduce the amount of energy and water to produce cash, and thus, contribute to better environmental and public health), the success of FRP inarguably depends on its continuance use intention, a topic that remains underexplored in information systems. Considering that behaviours are formed sequentially, this study adopts the cognitive appraisal theory and develops a model that includes primary and secondary appraisals alongside the moderating effect of perceived technology security to theories the continuance use intention of FRP. Usable data was collected from 350 respondents with FRP experiences through an online survey and analysed using PLS-SEM. Taken collectively, this study contributes theoretical and managerial insights for maximizing and sustaining the promise and utility of FRP.
Textile Inductive Resonant Wireless Link for Movement Recognition

This paper presents a textile inductive resonant wireless link for joint flexion recognition. The proposed system consists of a transmitting and a receiving resonators and two relays. Results obtained for the elbow are presented and discussed. It is demonstrated that the elbow flexion angle can be identified by observing the transmission coefficient of the inductive wireless link.
An Introduction of Explainable Artificial Intelligence to Winter Sports Performance Analysis

In recent years, the utilization of machine learning techniques has revolutionized performance analysis in the field of sports. However, a crucial drawback of these algorithms is their inherent black box nature, which hinders stakeholders' ability to comprehend the underlying rationale behind their decisions. This lack of transparency presents challenges for coaches, trainers, and athletes who heavily rely on analysis outcomes to shape their strategies and training approaches. To address this limitation, this paper introduces the concept of Explainable Artificial Intelligence (XAI) to sports performance analysis. We employ SHAP (SHapley Additive exPlanations), a model-agnostic explainable method, to provide a clear and interpretable representation of the contribution of different features towards the final model prediction. Ski jumping is chosen as the initial test case to illustrate the effectiveness of this approach.
Transcriptomic Investigation of Bioeffects from Microbubble and Focused Ultrasound Assisted Blood Brain Barrier Opening

Microbubbles stimulated by focused ultrasound is a rapidly emerging technique for noninvasively permeabilizing the blood brain barrier (BBB) for the delivery of therapeutic agents. Despite its prevalence, much is still unknown regarding the bioeffects of blood brain barrier opening (BBBO), such as the sterile inflammatory response (SIR). Testing a range of 4 microbubble volume doses (MVD) and 3 mechanical indices (MI), our study aims to investigate how these two parameters influence BBBO and the subsequent SIR RNA transcriptomic responses following permeabilization. Our results highlight that MVD and MI both influence the onset and degree of BBBO, with higher MVD and MI exhibiting the greatest degree of agent delivery compared to lower doses. Additionally, RNA sequencing analysis revealed that the most enriched gene sets are indeed associated with an inflammatory response. Through analysis of BBBO volume and RNA transcriptomics, a therapeutic window was established and indicated a region between BBBO and the onset of SIR where the BBB is permeabilized without significant inflammation. These results may help to standardize BBBO parameters to improve the safety and efficacy of drug delivery into the brain and confront gaps in knowledge regarding the biological mechanisms behind BBBO.
A Time Synchronization Method Based on Clustering Algorithm for Industrial Time-Sensitive Networks

The Industrial Internet communication system based on time-sensitive networks has the characteristics of high speed and real-time, in which the fundamental basis for data communication or network scheduling of any network node is its internal clock. In order to minimize errors between internal clocks, precise time synchronization needs to be performed. To solve the load imbalance problem caused by time synchronization in large-scale metropolitan area time-sensitive networks, this paper proposes a time synchronization method based on clustering algorithm. Simulation shows that the proposed method can effectively balance the network load with promising performances.
Inter-Vector Interference Self-Cancellation Scheme for Differential OSDM in Underwater Acoustic Communications

Differential orthogonal signal-division multiplexing (OSDM) is attractive for underwater acoustic (UWA) communications because it can eliminate channel estimation, resulting in a substantial reduction of complexity at the receiver. However, when the channel is time-varying, it may suffer from serious inter-vector interference (IVI), which is similar to inter-carrier interference (ICI) in differential orthogonal frequency-division multiplexing (OFDM). To mitigate this degradation of system performance, this paper provides a novel two-hop differential OSDM system based on IVI self-cancellation. Although this method improves system reliability at the cost of losing data rate, it is easy to implement in UWA modems. Finally, numerical simulations demonstrate the effectiveness of the proposed two-hop differential OSDM system over time-varying UWA channels.
La rugosidad de las superficies: Topometría

The importance of accurate roughness measurements is discussed in this work, the most common techniques to record topographic profiles are described and the most popular parameters used to quantify roughness are presented. A brief discussion on the self-affine character of heigth profiles is also presented. The fact that both the average roughness and the rms roughness depended on the sample length and hence cannot be considered as surface properties is emphasized. The use of the roughness exponent in modern statistical topometric methods is also included in this discussion.
TRIFIN: Hybrid Indoor Positioning with Bluetooth Internet of Things Gateways

TRIFIN is being proposed as a hybrid solution for determining locations of wireless devices at indoor facilities using Bluetooth Low Energy (BLE) technologies. Fingerprinting and trilateration methods are combined with signal correction algorithms for BLE tags. Multiple Internet of Things (IoT) gateway signal receivers are deployed on a floor plan, which is divided into sectors, and the signal levels received by each IoT-gateway from all sectors are stored in a fingerprinting database. The potential locations of mobile devices are estimated with respect to the fingerprinting database values and trilateration methods by utilizing a ranked voting model on the location estimation results of at least three IoT-gateways.
A Novel SAW Resonator with Anti-Groove Structure Based on Heterogeneous Substrate

This paper presents a novel SAW resonator with an anti-groove structure based on a heterogeneous substrate. The two-dimensional analysis is performed to investigate the variation mechanisms caused by the anti-groove structure, using the multi-physics software COMSOL. Different types of resonators (wavelength λ ranges from 4 μm to 12 μm, with a step of 1 μm) are designed for comparison. The resonator with the anti-groove structure is improved in both Q and ${\text{k}}_{{\text{eff}}}^2$, where the Q is improved by about 10% and the ${\text{k}}_{{\text{eff}}}^2$ is improved by about 3%. And the overall figure of merit value is improved by 10%-20%. Besides, a potential process idea for fabricating the novel SAW resonator with an anti-groove structure is provided. The lithium niobate (LN) thin film with an anti-groove structure can be transferred from the LN substrate to the sapphire substrate using ion implantation and bonding techniques.
Design and Analysis of mm-Wave MIMO SIW Antenna for Multibeam 5G Applications

This paper presents a slotted substrate integrated waveguide (SIW) multiple input multiple output (MIMO) antenna architecture for mm-Wave/mid-band 5G/6G satellite-cellular applications. An Expo-Gaussian statistical probability density function (PDF) has been introduced as an offset function with rotation to the radiating slots of the proposed MIMO SIW structure. To realize multidirectional multibeam MIMO antenna characteristics, both orthogonal (90°) and out-of-phase (180°) port switching technique has been established. Equivalent MIMO parameters have been realized through the S-parameters of the proposed MIMO architecture. A closed-form mathematical relationship has been established to characterize the MIMO parameters in the defined frequency band of interest. The proposed PDF rotation function enables the sidelobe level (SLL) and half-power beamwidth (HPBW) to be reduced to −20 dB and 8.5° respectively.