Miracle-dz/UniFAR-FTUs huggingface dataset:
SEISMIC IMAGING OF HIGH SLOPE STRUCTURES USING ONE-WAY WAVE EQUATION MIGRATION TECHNIQUES

ABSTRACT. The two-way wave equation deal with the wavefield in all of its forms, including multiples, refractions and internal reflections in more than one layer. Disregarding the downgoing wavefield, that is, considering only the propagation of the upgoing wavefield, starts using the one-way wave equation and some of these events are not considered. Thus, in areas of simple geology, the solution of the one-way wave equation is a good approximation. However, if the geology is complex, with abrupt lateral velocity variations and sub-vertical interfaces, such as in the presence of salt domes, methods of migration employing the one-way wave equation fail in imaging this kind of structures. In this paper, we present a way to overcome this limitation, by propagating the downgoing wavefield in following step upgoing wavefield. From these two extrapolated wavefields we apply four different imaging conditions, generating four intermediate sections, and the migrated section is formed from the weighted sum between the previous intermediate sections. This migration method was tested in two geological models (vertical fault and in a representative section of the Santos basin) and was able to reconstruct the complex structures existing in the models. Keywords: seismic migration, Fourier domain, sub-vertical reflectors. RESUMO. A equacao completa da onda trata da propagacao do campo de ondas em todas as suas formas, incluindo multiplas, refracoes e reflexoes internas em mais de uma camada. Ao desprezar o campo de ondas descendente, ou seja, ao considerar a propagacao apenas do campo ascendente, passa-se a empregar a equacao unidirecional da onda e parte destes eventos nao sao considerados. Desta forma, em areas de geologia simples, a solucao da equacao unidirecional e uma boa aproximacao, entretanto, caso a geologia seja complexa, apresentando variacoes laterais bruscas de velocidade e interfaces subverticais, tal como na presenca de domos salinos, os metodos de migracao que empregam a equacao unidirecional falham no imageamento das estruturas. Neste trabalho, apresenta-se uma forma de transpor esta limitacao, atraves da propagacao do campo de ondas descendente, numa etapa seguinte a do campo ascendente. A partir desses dois campos extrapolados, aplicam-se quatro condicoes de imagem distintas, gerando quatro secoes intermediarias, e a secao migrada sera formada a partir da soma ponderada entre as secoes anteriores. Este metodo de migracao foi testado em dois modelos geologicos (falha vertical e secao tipo da bacia de Santos) e mostrou-se capaz de reconstruir as estruturas mais complexas existentes nos modelos. Palavras-chave: migracao sismica, dominio de Fourier, refletores subverticais.
Active Disturbance Rejection Control: Tuning by PSO Considering Stability Conditions

This article presents the optimal tuning of an Active Disturbance Rejection Controller (ADRC) applied to the tracking control of a servo system. The ADRC consists of a Luenberger Observer coupled with a Disturbance Observer. Its purpose is to reject the disturbances affecting the servo system and to impose a desired closed-loop dynamics. Previous results on this controller focus only on its stability analysis. Moreover, finding the controller parameters that provide optimal performance is difficult. For the foregoing reasons, this work proposes using the Particle Swarm Optimization (PSO) algorithm to tune the parameters of the ADRC. The restrictions imposed on the particles are obtained from the stability analysis of the ADRC. This allows discarding those solutions leading to closed-loop instability. Therefore, the algorithm delivers solutions where a fitness function is minimized, and the closed-loop system is stable. Finally, realtime experiments on a laboratory prototype show the performance of the proposed tuning method.
A Study on the Resiliency of Coffee Roaster Business in Northern Thailand Region

Today's coffee shop and coffee roastery businesses are more mushrooming from the past. The coffee supply chain in Thailand, especially in the northern region, produces the most Arabica coffee in the country. In the last few years, the demand and coffee consumption volume has been higher than the output, and any disruption at any stage can directly impact the firm. This paper aims to prioritize the significant factors by using the fuzzy analytic hierarchy process (Fuzzy AHP) method based on the supply chain resilience concept under two dimensions, vulnerable and respond capacity, characteristic of supply chain resilience. This article will help researchers to understand the resilience concepts by prioritizing factors that influenced the coffee roastery and any related field under the supply chain resilience concept to prepare and resist possible disruption and unpredicted events.
Unveiling the Black Box: Investigating the Interplay between AI Technologies, Explainability, and Legal Implications

Discovering patterns is the process of understanding, while explainability refers to the ability to represent the discovered patterns in a way that the target audience can comprehend. If we adapt the relevant proposition to explainability in law, jurists initially extract the essential components from a concrete case and then establish meaningful connections among these components to reach a conclusion. The second stage, where they explain how they reached the conclusion through the connections established between the components, is referred to as generating reasoning. This study focuses on the first stage of explainability, which involves extracting significant components and establishing basic-level connections with the conclusion. The proposed methodology presents an explainability approach that is independent of the model and can be applied to all classification problems. Furthermore, the best performance in the literature is achieved on the task of predicting the outcomes of Turkish Constitutional Court decisions.
THE SPATIAL DISTRIBUTION OF THE b-VALUES BEFORE LARGE AND MODERATE EARTHQUAKES

In this paper the spatial distributions of the b-values before thirteen large and moderate earthquakes occurred in different regions of China are studied. It was found that in the vicinity of the epicenters there exist the areas of low b-values, outside of it are the areas of high b-values. The empirical relationship between the radius of such spatial distribution of the b-values and the magnitude of the main shock are evaluated, as shown by the formulas (1) (2) in the text. These can be used to determine the size of the earthquake preparation region, and the area in the computation of the variation of the b-values with time. It may be also used to predict the magnitude of the large shock occurred in future. Also, the regularity of the variation of the b-values before the large and moderate shocks are further verified, by a similar analysis of seismological data collected for the Yongshan (永善) and Luhuo (炉霍) shocks in southwest China.
Escherichia coli as Microbiological Quality Water Indicator:A High Importance for Human and Animal Health

The purpose of this review is to demonstrate the importance of monitoring microbiological quality in water for animals and humans, which relies primarily on coliforms, mostly Escherichia coli. Fecal coliforms, such as E. coli, are more specific indicators of fecal pollution. A fecal contamination detection can be performed not just in drinking water, but also in the environment. As a result of improved detection methods for E. coli, drinking water is becoming more reliable as a result of the use of E. coli as an indicator of fecal pollution. Drinking water contamination by feces is currently best detected by E. coli. Accordingly, temperate environments are more likely to have fecal coliforms than tropical environments, human and animal feces have high levels of E. coli in comparison to other fecal coliforms, and E. coli detection methods are more affordable, fast, sensitive, specific, and easy to use than other fecal coliforms.
Fabrication of a Cost-effective Piezoresistive Pressure Sensor Based on PVC/Reduced Graphene Oxide (rGO) Composite

A cost-effective piezoresistive sensor based on PVC/Reduced graphene oxide (rGO) was fabricated and its performance was investigated . The weight percent range from 0.1 to 30% of rGO in PVC matrix was studied. Composite parts were prepared by using the solution casting method from tetrahydrofurane (THF) solvent followed by solvent evaporation. The plot of electrical conduction versus rGO percentage was constructed to obtain the percolation threshold concentration. It was found that the percolation threshold of rGO leading to a continuous stable electrical conductivity in PVC matrix is about 25% beyond which electrical resistance was reduced from about 800 GΩ to lower than 100 KΩ range. The relative changes in electrical resistance of prepared polymer parts as a result of impact (stress), stretch and bending deformation were studied. The results showed that the fabricated composite can be used for sensing and/or monitoring and measurement of any mechanical displacement with high sensitivity, promising reproducibility and satisfactory durability. It must be mentioned that, during impact tests of polymer composites, a small piezoelectric effect was also observed for which further complimentary studies are being planned to be performed in near future in order to better understand this effect and its underlining molecular basis.
Epileptic seizures prediction of pediatric subjects in health-care pervasive environments

The pervasive healthcare system aims to provide health services to the end-user. Recording the electroencephalograph (EEG) data out of clinical/laboratory conditions and its wireless transmission to the data port enables the environment for real-time healthcare services. We propose a novel method for epileptic seizure prediction based on supervised machine-learning techniques. The method consists of simple signal preprocessing with only three feature extraction, and a random-forest classifier that recognizes the change in brain activity heralding the seizures (preictal state). The proposed method fulfilled the challenges of the pervasive healthcare system such as robustness to the presence of the artifact, stationarity of time series, and execution speed. The proposed method is verified on publicly available CHB-MIT EEG datasets recorded on pediatric subjects. The achieved averaged sensitivity and specificity are 92.62 % and 90.8 %, respectively. The proposed method is compared to eight state-of-art methods designed for clinical applications, with similar performance in one case, outperforming the remaining seven.
Dynamic Representative Data Management of Multiple Time Series for Edge Computing

With applications demanding increased amount of data, edge computing, where data are processed on the edge, is gaining popularity. Edge devices can store a limited amount of data and need to extract important data from the collected data. On-line interpolation-based data management (O-IDM) is an efficient method for managing time series data on an edge. O-IDM extracts and stores data to represent its overall shape. However, using this method becomes computationally expensive when the amount of the retained data is large. In this paper, we propose fast-O-IDM (FO-IDM), which can perform the same data extraction as O-IDM at a lower computational cost. Furthermore, we propose dynamic representative data management (D-RDM), which is a multiple time series management method that applies FO-IDM. D-RDM can efficiently hold the characteristics of multiple time series by dynamically controlling the capacity based on the data features. The extraction accuracy and computational costs of D-RDM are evaluated via comparison with other management methods. The experimental results confirmed that D-RDM could extract the data more accurately than the other methods, reducing the data amount required to convey the feature of the data by up to 25.0 percentage points compared to naive O-IDM. In addition, when the number of time series and the amount of the retained data were 3 and 30,000 respectively, the processing time of D-RDM was reduced by 91.2 percentage point compared to O-IDM, indicating that the computational cost of D-RDM is sufficiently small for use in edge devices.
Wearable Guide System for Obstacle Avoidance Based on Stereo Computer Vision

wearable Abstract: ETA (Electronic Travel Aids) is an important method to guide the visual impaired to navigate, in which avoiding the obstacle effectively is the most essential content. A novel wearable ETA system and corresponding algorithms for obstacle avoidance based on binocular stereo computer vision were proposed in this paper. By utilizing RANSAC(Random Sample Consensus) algorithm, the equation of pavement plane is extracted from the images collected by the binocular cameras, and the obstacles are distinguished according to their height relative to the pavement plane, and obstacles coordinates relative to the blind in the world coordinate system are calculated and determined. This presented appliance can provide the information of walkable pavement and safe direction where there is no obstacle. The algorithms were verified by using the experimental prototype. The results show that it can detect obstacles effectively and stably with strong robustness, which provides good foundation for developing blind guide equipment further.
Optimal Control Problems and Cross-Platform Instrumental Software Related to Constellation Cooperative Control

Currently, there is a trend to use constellations of small satellites to solve problems in space in a more “smart”, “adaptive” and cheap way than the standard one related to big and expensive satellites. More satellites mean more collision problems and more data, so we present some results related to the optimal solution of constellation formation and constellation support tasks. Our interest focus on two main fields: first of all, constellation formation w.r. t. intermediate constraints related to payloads decoupling from the carrier; secondly, cooperative avoidance problem with the state-constrained way to transform the “maximin” cost related to inter-satellite distance. We also demonstrate a tool for mission analysis - MIDE (Missions Integrated Development Environment), a cross-platform c++-written software.
Chatbots Put to the Test in Math and Logic Problems: A Comparison and Assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard

In an age where artificial intelligence is reshaping the landscape of education and problem solving, our study unveils the secrets behind three digital wizards, ChatGPT-3.5, ChatGPT-4, and Google Bard, as they engage in a thrilling showdown of mathematical and logical prowess. We assess the ability of the chatbots to understand the given problem, employ appropriate algorithms or methods to solve it, and generate coherent responses with correct answers. We conducted our study using a set of 30 questions. These questions were carefully crafted to be clear, unambiguous, and fully described using plain text only. Each question has a unique and well-defined correct answer. The questions were divided into two sets of 15: Set A consists of “Original” problems that cannot be found online, while Set B includes “Published” problems that are readily available online, often with their solutions. Each question was presented to each chatbot three times in May 2023. We recorded and analyzed their responses, highlighting their strengths and weaknesses. Our findings indicate that chatbots can provide accurate solutions for straightforward arithmetic, algebraic expressions, and basic logic puzzles, although they may not be consistently accurate in every attempt. However, for more complex mathematical problems or advanced logic tasks, the chatbots’ answers, although they appear convincing, may not be reliable. Furthermore, consistency is a concern as chatbots often provide conflicting answers when presented with the same question multiple times. To evaluate and compare the performance of the three chatbots, we conducted a quantitative analysis by scoring their final answers based on correctness. Our results show that ChatGPT-4 performs better than ChatGPT-3.5 in both sets of questions. Bard ranks third in the original questions of Set A, trailing behind the other two chatbots. However, Bard achieves the best performance, taking first place in the published questions of Set B. This is likely due to Bard’s direct access to the internet, unlike the ChatGPT chatbots, which, due to their designs, do not have external communication capabilities.
Vivaldi MIMO Antenna Using Oval Shape Structure tor for 24GHz-34GHz Millimeter-Wave Communication Frequency Band

The increasing proliferation of advanced devices for wide-band, 5G communication, micrometer wave, and millimeter wave communication necessitates an antenna that can handle massive data rates, provides high gain, and has a stable radiation pattern. Most researchers are interested in the Vivaldi antenna because of its high gain, wide bandwidth, low radiation loss, and radiation pattern stability. Recently, the evolution and demand for millimeter wave devices and antennas are increasing for their use in different applications and future millimeter wave communication. This article presents a compact, planer, efficient $\mathbf{2}\times \mathbf{1}$ Vivaldi MIMO antenna for 24GHz-34GHz frequency band millimeter wave communication. This paper mainly focuses on the small size of the antenna which is $\mathbf{31}\times \mathbf{20}\mathbf{mm}\mathbf{2}$ and isolation is 50dB. The top side is made up of an Asymmetric flared shape patch with an oval shape structure, while the back side is made up of partial ground with flare shaped structure to achieve minimum mutual coupling and compact size. For high bandwidth and isolation, the Vivaldi structure is used on a low-cost FR4 dielectric substrate with a loss tangent of 0.02. The simulated envelope correlation coefficient is 0.001 which is very low according to the standard value of the International Telecommunication Union (ITU). The simulated response for the proposed antenna is reported in terms of gain, efficiency, surface current distribution, VSWR, return loss and directivity and it shows the good performance characteristics of the MIMO antenna. Both the simulated and measured results are presented and it shows good agreement between them.
Super-Resolution Estimation of Delay-Doppler-Angle Parameters of Doubly Selective Channels in Hybrid MIMO Systems

This paper studies the estimation of delay-Doppler-angle (DDA) parameters of doubly selective fading channels in hybrid multiple-input multiple-output (MIMO) communication systems. Based on low-rank matrix completion (LRMC) and the root-MUSIC algorithm, we develop time-domain super-resolution schemes for progressive estimation of the angles, Doppler shifts, delay shifts and gains of the physical propagation paths from the sampled observations at the hybrid receiver. The proposed solution is shown to be able to achieve high-accuracy estimation of the parameters of the physical channels, which can in turn be used to reconstruct and predict the channel states, e.g., in the delay-Doppler or delay-time domains.
Adaptive Control for High-Performance Trajectory Tracking in Multirotor UAVs

This paper presents an adaptive position control law to achieve high-performance trajectory tracking in Unmanned Aerial Vehicles (UAVs). Considering a hierarchical control scheme, the main contribution of this work is the design of an adaptive augmentation of a baseline position controller to account explicitly for aerodynamic effects. The adaptive augmentation allows for maintaining high-performance in situations where the baseline controller was not designed for or performs poorly. The results of experimental tests conducted using a small quadrotor are reported in order to compare the performance of the proposed design with respect to state-of-the-art non-predictive feedback controllers.
Effectiveness of Technology-Based Interventions for School-Age Children With Attention-Deficit/Hyperactivity Disorder: Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background Attention-deficit/hyperactivity disorder (ADHD) is relatively common among school-age children. Technology-based interventions, such as computer-assisted training programs, neurofeedback training, and virtual reality, show promise in regulating the behaviors and cognitive functions of children with ADHD. An increasing number of randomized controlled trials have been conducted to evaluate the effectiveness of these technologies in improving the conditions of children with ADHD. Objective This study aims to conduct a systematic review of technological interventions for school-age children with ADHD and perform a meta-analysis of the outcomes of technology-based interventions. Methods A total of 19 randomized controlled studies involving 1843 participants were selected from a pool of 2404 articles across 7 electronic databases spanning from their inception to April 2022. ADHD behaviors, cognitive functions, learning ability, and quality of life were addressed in this study. Results Random effects meta-analyses found that children with ADHD receiving technology-based intervention showed small and significant effect sizes in computer-rated inattention (standardized mean difference [SMD] −0.35; P<.04), parent-rated overall executive function measured by the Behavior Rating Inventory of Executive Function (SMD −0.35; P<.04), parent-rated disruptive behavior disorder measured by the Child Behavior Checklist (SMD −0.50; P<.001) and Disruptive Behavior Disorder Rating Scale (SMD −0.31; P<.02), and computer-rated visual attention measured by the Continuous Performance Test (SMD −0.42; P<.001) and Reaction Time (SMD −0.43; P<.02). Conclusions Technology-based interventions are promising treatments for improving certain ADHD behaviors and cognitive functions among school-age children with ADHD. Trial Registration PROSPERO CRD42023446924; https://tinyurl.com/7ee5t24n
The Role of Personality Peculiarities on Depression and Anxiety of Medical Doctors Using the Job-Demands Resources Model: The Mediating Effect of Job Burnout

Prolonged stress also known as job burnout refers to psychological conditions resulting from excessive fatigue due to work-related factors. However, distinguished features in personality might affect how people cope with job burnout which has implications for their mental health especially for their depression and anxiety. Therefore, this study intends to respond to the existing practical and empirical gaps by investigating the relationships between personality differences such as neuroticism and extraversion and mental health with the role of job burnout as the mediator. A cross-sectional study was conducted at eight public hospitals within Peninsular Malaysia. A total of 471 medical doctors from six departments participated. The result has shown that job burnout mediates the relationship between neuroticism and mental health. Job burnout made no difference to associations between extraversion and mental health.