Miracle-dz/UniFAR-FTUs huggingface dataset:
Wavelet analysis of geomagnetic jerks

Geomagnetic jerks are singular events abrupt changes in the slope of the secular variation in the earth’s magnetic field on time scales of the order of a year. Although discovered over 30 years ago, geomagnetic jerks remain poorly understood. Their origin lies in the magnetohydrodynamics that is responsible for the maintenance of the earth’s magnetic field produced within the earth’s liquid and convecting core (through a so-called dynamo process). A wavelet tool was programmed in MATLAB and a wavelet analysis applied to detect and characterize jerks in the time series made of monthly mean values of the east component of the geomagnetic field from selected worldwide observatories. The analysis was first performed on various synthetic series comprising the sum of: a “main” signal of smooth variation intervals separated by singular events; an “external” signal made up of the sum of a few harmonic signals; and white noise. Conditions are highlighted in which the singular events could be clearly isolated and their associated “regularities” recovered. The most pronounced geomagnetic jerks were detected globally in 1969 and 1978. Other jerks were detected, but were not detected everywhere at the earth’s surface. The spatio-temporal behaviour of jerks, where jerks arrive in the northern hemisphere before the southern hemisphere, is explored. It has also been demonstrated that the jerks are more singular than previously supposed; their “regularity” is closer to 1.5 than to 2. The wavelet analysis was used also to investigate whether the nature of jerks could be classified into one of the following categories: geomagnetic jerks being the result of some instability starting at the time of the jerk or, geomagnetic jerks being the result of some continuous oscillations or, that geomagnetic jerks could also possibly reflect a process ending at the time of the jerk.
Problemas de las TICs en el Museo Contemporáneo

El advenimiento de las Nuevas Tecnologias de la Informacion y Comunicacion (TICs), ha originado profundas transformaciones ideologicas, politicas, economicas y socio-culturales en la contemporaneidad. Siguiendo las directrices de la Sociedad de la Informacion, la institucion museistica ha abierto sus puertas a la revolucion electronica, que remueve desde los cimientos del organigrama funcional hasta su propagacion virtual, paralelo a las recientes vias comunicativas e intereses de la comunidad. En una doble perspectiva, la del museo y el publico, nos plantearemos las ventajas e inconvenientes de la apuesta tecnologica.
Learning Sub-Dimensional HRTF Representations Towards Individualization Applications - Traditional and Deep Learning Approaches

Individualized Head Related Transfer Functions (HRTFs) are indispensable in order to accurately reproduce spatial audio over headphones. Encoding the high-dimensional HRTFs to a sub-dimensional space has proven to be useful in many previous research efforts in predicting individualized HRTFs. In this work, we provide a comparative study of some traditional methods such as Principle Component Analysis (PCA) or Multi-Layer Perceptron (MLP) based Autoencoders and the more recent generative deep learning approaches such as a Convolutional Neural Network (CNN) based Vector Quantized Variational Autoencoder (VQ-VAE) for learning HRTF representations. We further demonstrate the benefits of using 3D-CNNs for this task to learn correlations between neighboring HRTFs, along both spatial and frequency dimensions. To this end, we provide evidence suggesting that such a 3D-CNN based approach enables the derived latent space to encode features more representative of the individuality of the HRTFs while also allowing for the representations to be significantly more compact. Finally, we also explore the advantages of such robust representations towards downstream applications of predicting Individualized HRTFs.
Exploring how the balanced scorecard engages and unfolds

Research on the Balanced Scorecard (BSC) has questioned whether its adoption generates greater integration between financial and non-financial performance measures, supports strategy implementation, increases performance and improves strategic decision making. This research has also highlighted how the BSC often generates effects other than these. Building on studies that portray Performance Measurement Systems (PMSs) as intrinsically incomplete practices of representation, the purpose of this paper is to investigate how the BSC engages users due to the organizing work that it stimulates around this incompleteness. Our findings allow us to further articulate the power of specific visual elements of the BSC, such as hierarchical trees, wheels, causal and strategy maps. The paper provides material that contributes to a better understanding of how the BSC performs multiple roles within organizations beyond a simple representational functionality and unfolds continuously. It contributes to the growing literature on accounting inscriptions, i.e. signs producing incomplete representations, by developing an analytical theoretical framework that defines the BSC as a rhetorical machine composed of four key features: (1) a visual performable space (i.e. a schema generating creative engagement); (2) a method of ordering and innovation; (3) a means of interrogation and mediation; and (4) a motivating ritual.
Energy Analysis of Hydrotreated Vegetable Oil as Renewable Fuel Using Chlorella Vulgaris Using Aspen Energy Analyzer

Due to limited literature on hydrotreated vegetable oil (HVO) production using third-generation feedstock, the study was proposed to investigate the energy analysis using microalgae Chlorella Vulgaris. The plant simulation was performed using ASPEN HYSYS, while heat integration was performed using ASPEN Energy Analyzer. Utilizing the problem algorithm table (PTA), hot and cold pinch was determined to be 173.1°C and 163.1°C, respectively. Two cases were proposed, i.e. (1) heat exchanger network (HEN) Grassroot case and (2) heat exchanger network (HEN) retrofit case. For the first case, a new heat exchanger (E-107) is implemented in exchange for one reduction on the cooler unit (E-103). In contrast, the second case introduces two new heat exchangers (E-107 and E-108) with one cooler unit (E-103) reduction but lowered heating duty by around 7.08%, while cooling duty was reduced by 14.78% compared to the grassroots design. Although retrofits have more heat exchangers, the operating cost was saved up to 7.200$\times 10^{-3}$ ${\$}$/s. This indicates both flaws and benefits to retrofit design; hence, the implementation should consider the trade-off between the plant’s capital and operating costs and be justified once the additional cost was offset throughout the operating plant life after a payback period of 0.5846 years.
MeG2: In-Memory Acceleration for Genome Graphs Analysis

Genome graphs analysis has emerged as an effective means to enable mapping DNA fragments (known as reads) to the reference genome. It replaces the traditional linear reference with a graph-based representation to augment the genetic variations and diversity information, significantly improving the quality of genotyping. The in-depth characterization of genome graphs analysis uncovers that it is bottlenecked by the irregular seed index access and the intensive alignment operation, stressing both the memory system and computing resources.Based on these observations, we propose MeG2, a lightweight, commodity DRAM-compliant, processing-in-memory architecture to accelerate genome graphs analysis. MeG2 is specifically integrated with the capabilities of both near-memory processing and bitwise in-situ computation. Specifically, MeG2 leverages the low access latency of near-memory processing with the index-centric offload mechanism to alleviate the irregular memory access in the seeding procedure, and harnesses the row-parallel capacity of in-situ computation with the distance-aware technique to exploit the intensive computational parallelism in the alignment process. Results show that MeG2 outperforms the CPU-, GPU-, and ASIC-based genome graphs analysis solutions by 502× (30.2×), 272× (15.1× ), and 5.5× (8.3×) for short (long) reads, while reducing energy consumption by 1628× (85.6×), 1443× (77.1×), and 7.8× (11.7×), respectively. We also demonstrate that MeG2 offers significant improvements over existing PIM-based genome sequence analysis accelerators.
Fake News Detection: A Brief Investigation Into the State-of-The-Art Approaches and A Mixed Language Dataset

In the era where massive information can be spread easily through social media, fake news detention is increasingly used to prevent widespread misinformation. Machine learning algorithms have been used to identify patterns in news content, and databases are built to filter the false information. This paper investigates the state-of-the-art development into this research domain. A brief review is presented, from public domain dataset to various machine learning models. In addition, a mixed language dataset is presented for future researchers and investigators in this area.
Relative Transfer Function Vector Estimation for Acoustic Sensor Networks Exploiting Covariance Matrix Structure

In many multi-microphone algorithms for noise reduction, an estimate of the relative transfer function (RTF) vector of the target speaker is required. The state-of-the-art covariance whitening (CW) method estimates the RTF vector as the principal eigenvector of the whitened noisy covariance matrix, where whitening is performed using an estimate of the noise covariance matrix. In this paper, we consider an acoustic sensor network consisting of multiple microphone nodes. Assuming uncorrelated noise between the nodes but not within the nodes, we propose two RTF vector estimation methods that leverage the block-diagonal structure of the noise covariance matrix. The first method modifies the CW method by considering only the diagonal blocks of the estimated noise covariance matrix. In contrast, the second method only considers the off-diagonal blocks of the noisy covariance matrix, but cannot be solved using a simple eigenvalue decomposition. When applying the estimated RTF vector in a minimum variance distortionless response beamformer, simulation results for real-world recordings in a reverberant environment with multiple noise sources show that the modified CW method performs slightly better than the CW method in terms of SNR improvement, while the off-diagonal selection method outperforms a biased RTF vector estimate obtained as the principal eigenvector of the noisy covariance matrix.
Inclusive electron scattering from nuclei in the quasielastic region at large momentum transfer

Experiment E02-019, performed in Hall C at the Thomas Jefferson National Accelerator Facility (TJNAF), was a measurement of inclusive electron cross sections for several nuclei ($^{2}$H,$^{3}$He, $^{4}$He, $^{9}$Be,$^{12}$C, $^{63}$Cu, and $^{197}$Au) in the quasielastic region at high momentum transfer. In the region of low energy transfer, the cross sections were analyzed in terms of the reduced response, F(y), by examining its $y$-scaling behavior. The data were also examined in terms of the nuclear structure function $\nu W_2^A$ and its behavior in $x$ and the Nachtmann variable $\xi$. The data show approximate scaling of $\nu W_2^A$ in $\xi$ for all targets at all kinematics, unlike scaling in $x$, which is confined to the DIS regime. However, $y$-scaling observations are limited to the kinematic region dominated by the quasielastic response ({$y<$0}), where some scaling violations arising from FSIs are observed.
Understanding the Key Drivers of Intentions to Use Digital Banking Services in Malaysia

Digital bank is an emerging trend in the global banking industry. Digital bank is the digitisation of every scheme and activity carried out by both the financial institution and its customers included. Malaysia is a cash-based society, so digital banking will be a disruptive digital transformation for Malaysians. Hence, the purpose for this study is to investigate the key factors affecting Malaysian consumers“ intention to use digital bank. The model included eight constructs, which are performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit and trust. On the other hand, the dependent variable in this study is intention to use. A total of 203 questionnaires were collected for this study and the data collected were analysed by Partial Least Square Structural Equation Modelling (PLS-SEM 4). The results of the study revealed that hedonic motivation and performance expectancy were significantly related to intention to use. This study will contribute to the government and developers to help the government and companies that will be involved in this area to understand the consumer's intention and concern towards digital bank and the direction of development and improvement of digital banking.
Barriers to providing lung-protective ventilation to patients with acute lung injury

Objective:No studies have explored the barriers to implementing lung-protective ventilation in patients with acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). Our objective was to identify barriers to using lung-protective ventilation in patients with ALI/ARDS. Design:Survey with content analysis of open-ended responses. Setting:Medical center. Participants:Experienced intensive care unit nurses and respiratory therapists network identified through purposive sampling at hospitals from the ARDS Network, a National Institutes of Health-sponsored research consortium. Interventions:Survey. Results:Fifty-five surveys representing all ten ARDS Network sites were received. Twenty-seven (49%) of the respondents were intensive care unit nurses, 24 (44%) were respiratory therapists, and four did not indicate their profession. Clinicians had used lung-protective ventilation in a median of 20 (interquartile range, 10–50) patients with ALI/ARDS. Respondents identified physician willingness to relinquish control of ventilator, physician recognition of ALI/ARDS, and physician perceptions of patient contraindications to low tidal volumes as important barriers to initiating lung-protective ventilation. Important barriers to continuing patients on lung-protective ventilation were concerns over patient discomfort and tachypnea and concerns over hypercapnia, acidosis, and hypoxemia. Techniques for overcoming barriers were identified including specific ventilator setup recommendations, clinician education, and tools to assess patient discomfort. Conclusions:Experienced bedside clinicians perceive important barriers to implementing lung-protective ventilation. Successful strategies to increase use of lung-protective ventilation should target these barriers.
Research on Distance Calculation Method of autonomous vehicle Based on Direct Sparse Vision

It is very important for autonomous vehicles to obtain their own accurate positioning. Currently, visual SLAM positioning technology is considered as the mainstream positioning means due to its rich scene information and low cost. In this paper, the principle of visual SLAM is analyzed. This article briefly introduces the direct method and feature point method in visual SLAM, outlines the general development status of visual SLAM, and ultimately chooses to conduct subsequent research on the basis of DSO-SLAM. Aiming at the problem of cumulative error, a monocular direct sparse visual odometry with loopback is proposed. Through the transformation of Bag Of Words (BOW) model, the loopback frame is found out, and more constraints are entered to optimize the pose, aiming at eliminating the accumulated errors in the long-term operation of the algorithm and improving the positioning accuracy. This article conducted comparative experiments with DSO on public datasets, verifying that in the presence of loopback scenarios, DSO-IMP can significantly eliminate cumulative errors and improve positioning accuracy. This article also conducted experiments and found that DSO-IMP can correctly detect loops in actual motion scenes, and use loops to correct trajectories, which has certain practicality.
Impacts of Connected Automated Vehicles on Urban Parking: A Literature Review

Due to limited parking space in metropolitan areas, parking management and design remain a major challenge in urban governance. The emergence and prevalence of connected automated vehicles (CAVs) offer good potential to address this challenge. For example, a CAV can drop off a commuter at the workplace and then autonomously find a parking spot; shoppers in a rush may choose to have a CAV cruise and wait rather than occupy a parking spot. The paper attempts to provide a comprehensive review of how CAVs may reshape parking management/policy, covering three topics: (i) impacts on parking behavior, e.g., parking at home, cruising for parking, etc. (ii) parking policies from the perspective of government and private operators, e.g., demand-side and supply-side strategies; (iii) impacts on parking land use, e.g., need and distribution of parking space, urban layout, etc. A holistic view of CAV parking-related macroscopic models, including bottleneck models, network models, and microscopic models (i.e., agent-based models), is also provided in the paper.
Lightweight DeepLabv3+ Segmentation Method for Substation Equipment Oil Leakage Image

To efficiently and accurately detect and identify oil leakage in the substations oil-filled equipments, this paper proposes a lightweight segmentation model based on the improved DeepLabv3+ model. By using the lightweight segmentation model based on the improved DeepLabv3+ model. By using the lightweight MobilenetV2 network as the backbone feature extraction network, the model reduces the number of network parameters and improves the semantic segmentation efficiency. To improve the segmentation accuracy of small leaked oil region and the edges, a feature pyramid structure is introduced to enhance the feature extraction ability. In addition, a dataset of oil leakage images from substation oil-filled equipments is constructed for the oil leakage image segmentation, and experimental tests show that the proposed algorithm significantly improves segmentation performance. Compared with the DeepLabv3+ algorithm-based oil leakage detection model, the average intersection-over-union (IoU) is improved by 1.28%, and the number of model parameters decreased to 15.5% of the original. It meets the practical requirements of edge deployment model, and has high practicality.
UpTime: Towards Flow-based In-Memory Computing with High Fault-Tolerance

Processing in-memory promises to accelerate data-intensive applications by breaking von-Neumann based design principles. Flow-based computing is an in-memory computing paradigm that has shown immense potential for executing Boolean logic. Unfortunately, the immature fabrication processes for nanoscale memristor crossbars still struggle with yield challenges and run-time defects, which may render the computing system non-functional. Even worse, no previous studies have investigated the fault-tolerance of flow-based computing systems, which could potentially limit the capabilities of the entire paradigm. In this paper, we propose the UpTime framework to provide guaranties on the functional correctness and to maximize the lifetime of flow-based computing systems. The framework utilizes data layout organization to mitigate errors from faults with known type and location. To handle defects occurring at run-time, we propose the use of an error detection signal that can be evaluated with low overhead. The experimental evaluation demonstrates that the UpTime framework is capable of guaranteeing functional correctness for an average of 15.24 years. The up-time to down-time ratio is 99.9992%. Compared with utilizing the state-of-the-art write-verify scheme, the proposed error signal reduces power consumption by 25% and increases throughput by 6%, respectively.
Building allometric models to estimate above-ground and below-ground biomass of mahogany sapling.

There are large areas of big-leaf mahogany (Swietenia macrophylla King) afforestation and reforestation plantations for reducing carbon dioxide due to climate change in Taiwan. In Taiwan, out of the total area of mahogany plantations of approximately 2982.00 ha, 125.40 ha is in national forests and 232.00 is at the Hsin-Hua Experimental Forest Station. Biomass is a plant attribute that accumulates over time. It is an important indicator of growth and is used in analysis and management processes. Above-ground biomass is the key parameter in many allometric relationships. However, there are few studies on below-ground biomass estimations of mahogany, for it is difficult to excavate and quantify these portions. The aim of this study was to establish an allometric relationship to estimate the above-ground (stem wood, stem bark, branches, and foliage) and belowground (roots) biomass using an easily measured value, such as the diameter at breast height (DBH), diameter at the stem base (DSB) and tree height (H). Forty-six mahogany saplings (0 cm ≤ DBH ≤ 10 cm), with different ages in the second compartment of this forest station, were used to establish the allometric functions of DBH and biomass, and functions of DSB and biomass. A significance test of the correlation was used to test the relationship between DBH and biomass in different sections, including foliage, branches, stems, above-ground, below-ground, and the entire tree. The DSB was also tested. The results showed that the power regression function was superior to other functions. The correlation between DBH and biomass was higher than the correlation between DSB and biomass. The allometric functions for the entire tree biomass, above-ground biomass, and below-ground biomass were W = 175.67×DBH^2.29 (R^2 = 0.9692), Wabove = 112.21×DBH^2.34 (R^2 = 0.9621), and Wbelow = 61.65×DBH^2.19 (R^2 = 0.9610), respectively. The carbon content of each part of mahogany trees was as follows: stem wood (45.83±0.92%), roots (45.09±0.89%), foliage (44.95±1.21%), branches (43.74±1.09%), and stem bark (42.64±1.01%). Managers can estimate the biomass, carbon content ratio, and carbon storage of mahogany without destroying trees.
Active Distribution Network with Distributed Energy Resources

— With the development of distributed energy resources (DERs) and the increasing flexibility in active distribution networks (ADNs), the economic operation of ADN coordinating both generation and demand sides is required to be studied. In this paper, considering the electricity price response of DER, a coordinated operation strategy for ADN is presented based on a bi-level agent framework. DER agent makes their own response based on the technical operability and economic consideration, while ADN agent will finally coordinate each participant by using the interactive benefit prioritization (IBP) principle. The simulation results indicate that the proposed strategy cannot only reduce the power imbalance, but also improve the economic operation of ADN. Moreover, consumption of renewable energy is ameliorated as well.