Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Evaluation of Various Models for the Classification of Music Genre

This project explores music genre differentiation using the GT-ZAN dataset, comprising ten genres. Ten classical machine learning models, including KNN, SVM, and Cross Gradient Booster, are compared using features such as MFCC mean, MFCC variance, genre labels, and root mean square values from the frequency and time domains. Experimental results demonstrate variations in model performance, with specific classifiers achieving higher accuracy and precision for particular genres, while others excel in recall and F1-score. The findings offer valuable insights for future music genre classification and contribute to the development of music genre categorization systems.
Monitoring of Liver RF Ablation Using UWB Radar: A Numerical Study

Thermal ablation is a well-known method used in interventional radiology to treat cancer. The main objective of this study was a numerical investigation of RF liver ablation monitoring with the UWB radar. The eight UWB bowtie antennas were uniformly distributed around the liver area. The antennas were excited by the Gaussian pulse with frequencies ranging from 1 to 6 GHz. The catheter model (with diameter of 2 mm) was placed into the tumor position within the liver. The three concentric ellipsoids-shaped ablation zones were placed around the catheter to simulate the real clinical situation From the recorded signals the clutter was subtracted and results were visualized using the “Delay and Sum” algorithm. A total three different tumor positions were considered within our study. This numerical analysis demonstrates the potential of the method to continuously monitor the ablation process using UWB radar. The ablation zones of three tumor positions were successfully reconstructed with a mean spatial precision lower than 2 mm. The results suggest that the UWB radar monitoring is applicable for continuous RF ablation monitoring with accuracy that is sufficient for therapeutic purposes.
Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country’s utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hybrid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical electric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation re-sults demonstrate the proposed algorithm enhanced better forecasting accuracy.
Energy-Saving Capture at Mars via Backward-Stable Orbits

The orbit capture, which transfers the spacecraft from the interplanetary trajectory to the target orbit about a celestial body, is a key event in an exploration mission. A low-energy capture strategy, termed ballistic capture, has been developed and applied to lunar transfer, such as Hiten [1] and GRAIL [2]. Such capture exploits the gravitational force of the multi-body system to change the orbit energy of the spacecraft with respect to the target planet from positive to negative.
Time Frequency Analysis of Heart Rate Variability with Chaos Theory

Heart rate variability (HRV) is the physiological phenomenon of variation in the time interval between heartbeats. It is measured by the variation in the beat-to-beat interval. HRV parameters providing information about the scaling behavior or the complexity of the cardiac system were included. In addition, Chaos theory is a field of study in mathematics, with applications in several disciplines including physics, engineering, economics, biology, and philosophy. Chaos theory studies the behavior of dynamical systems that are highly sensitive to initial conditions, an effect which is popularly referred to as the butterfly effect. The goal was to investigate the influence of gender, age and day-night variation on these nonlinear HRV parameters. Numerical titration yielded similar information as other nonlinear HRV parameters do. However, it does not require long and cleaned data and therefore applicable on short (9 minutes) noisy time series. A higher nonlinear behavior was observed during the night while nonlinear heart rate fluctuations decline with increasing age. Our results support the involvement of the autonomic nervous system in the generation of nonlinear and complex heart rate dynamics.
Inferring Compensatory Kinase Networks in Yeast using Prolog

Signalling pathways are conserved across different species, therefore making yeast a model organism to study these via disruption of kinase activity. Yeast has 159 genes that encode protein kinases and phosphatases, and 136 of these have counterparts in humans. Therefore any insight in this model organism could potentially offer indications of mechanisms of action in the human kinome. The study utilises a Prolog-based approach, data from a yeast kinase deletions strains study and publicly available kinase-protein associations. Prolog, a programming language that is well-suited for symbolic reasoning is used to reason over the data and infer compensatory kinase networks. This approach is based on the idea that when a kinase is knocked out, other kinases may compensate for this loss of activity. Background knowledge on kinases targeting proteins is used to guide the analysis. This knowledge is used to infer the potential compensatory interactions between kinases based on the changes in phosphorylation observed in the phosphoproteomics data from the yeast study. The results demonstrate the effectiveness of the Prolog-based approach in analysing complex cell signalling mechanisms in yeast. The inferred compensatory kinase networks provide new insights into the regulation of cell signalling in yeast and may aid in the identification of potential therapeutic targets for modulating signalling pathways in yeast and other organisms.
A Bayesian Framework for Estimating Weibull Distribution Parameters: Applications in Finance, Insurance, and Natural Disaster Analysis

This research presents a Bayesian framework for parameter estimation in the two-parameter Weibull distribution, with applications in finance and investment data analysis. The Weibull distribution is widely used for modeling stock pricing movements and making uncertain predictions in financial datasets. The proposed Bayesian approach assumes a gamma prior distribution for the scale parameter, with a known shape parameter. A simulation study using simulated financial data compares the Bayesian method with maximum likelihood estimators in terms of accuracy, error accumulation, and computational time across various sample sizes and parameter values. Results indicate the Bayesian approach performs similarly to maximum likelihood for small samples, while demonstrating computational efficiency for larger financial datasets. The proposed Bayesian model's application to simulated financial data showcases its practical relevance in real-world scenarios. This Bayesian framework offers a valuable tool for handling uncertainty and making informed decisions in financial data analysis, providing robust parameter estimation and uncertainty quantification in finance and investment domains.
Deep Carbonate Bank Margin Structure and Sedimentation in the Northern Bahamas

The deep 200 m carbonate bank margins in the northern Bahamas display complex and diverse seismic facies structure morphology and sedimentary facies On the basis of analysis of more than 1200 km of high frequency high resolution seismic reflection profiles and 149 bottom samples seven deep carbonate bank margin types have been recognized in the northern Bahamas I Type A Open Ocean Windward margin east of Little Bahama Bank LBB 2 Type B Open Ocean Windward margin north of LBB 3 Type C Extended deep bank margins off the northwest corners of both LBB and Great Bahama Bank GBB 4 Type D Open Seaway Leeward margin west of LBB and GBB 5 Type E Open Seaway Leeward margin south of LBB 6 Type F Open Seaway Windward margin north of GBB and 7 Type G Eroded margins along the mouth of the Northwest Providence Channel NWPC In general leeward deep bank margins tend to be steeper narrower more dissected and contain significantly greater amounts of coarse grained sediment than windward margins The processes most responsible for the development of deep carbonate bank margins include I basement faulting 2 direction and magnitude of off bank sediment transport 3 oceanic circulation 4 gravity and pelagic sedimentation 5 submarine cementation and 6 biological buildups Of these basement faulting is primarily responsible for the initiation of carbonate platform edges Offbank sediment transport is controlled by the physical energy flux winds waves storms at the sea surface and controls the availability of shallow water sediment for transport to the deep flanks Oceanic circulation bottom currents winnows and redistributes sediment and may aid in submarine cementation which appears responsible forthe stabilization of carbonate slopes through hardground development Pelagic carbonate sediments are ubiquitous in deep water carbonate environments but are volumetrically important only where they are not winnowed by bottom currents diluted by sediment gravity flow deposits or deposited below the CCD Much of the material deposited on carbonate slopes is allochthonous transported via a variety of sediment gravity flows including turbidity currents grain flows and debris flows Deep waterbiological buildups are locally important and may add to flank accretion Previous models of deep carbonate bank margins derived solely from analysis of the ancient appear to be oversimplifications A new knowledge of the diverse types of modern deep carbonate bank margins and an understanding of the processes that control their formation should aid in the interpretation of ancient deep water carbonate sequences as well as in theexploration forand exploitationof hydrocarbons from off platform carbonates
Cross Domain Correlation Maximization for Enhancing the Target Recognition of SSVEP-Based Brain–Computer Interfaces

The target recognition performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces can be significantly improved with a training-based approach. However, the training procedure is time consuming and often causes fatigue. Consequently, the number of training data should be limited, which may reduce the classification performance. Thus, how to improve classification accuracy without increasing the training time is crucial to SSVEP-based BCI system. This study proposes a transfer-related component analysis (TransRCA) method for addressing the above issue. In this method, the SSVEP-related components are extracted from a small number of training data of the current individual and combined with those extracted from a large number of existing training data of other individuals. The TransRCA method maximizes not only the inter-trial covariances between the source and target subjects, but also the correlation between the reference signals and SSVEP signals from the source and target subjects. The proposed method was validated on the SSVEP public Benchmark and BETA datasets, and the classification accuracy and information transmission rate of the ensemble version of the proposed TransRCA method were compared with those of the state-of-the-art eCCA, eTRCA, ttCCA, LSTeTRCA, and eIISMC methods on both datasets. The comparison results indicate that the proposed method provides a superior performance compared with these state-of-the-art methods, and thus has high potential for the development of a SSVEP-based brain-computer interface system with high classification performance that only uses a small number of training data.
Association and Expression of Cluster of Differentiation 4 (CD4) Gene in IPB-D2 Chicken Related to Immunocompetence Index

The CD4 gene plays an important role in the immune process by producing CD4 molecules that aid in producing antibodies. IPB-D2 chickens are selected from IPB-D2 chickens based on IgY concentration and ND antibody titer. This study aimed to analyze the polymorphism of the CD4 gene, unravel the mRNA expression of CD4 gene in IPB-D2 chicken related to the immunocompetence index, and detect the CD4 gene pathway. The total samples used were 100 IPB-D2 G2 chickens aged 21 weeks. Blood samples were collected for ELISA test, HI test, sequencing test, and seca tonsil tissue for relative mRNA expression. Polymorphism and association data were analyzed using MEGAX, FinchTV, SNPstat, and DNAsp. The relative mRNA expression analysis was conducted using qRT-PCR. The pathway analysis of the CD4 gene was performed using the Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis. The result showed there were 4 SNPs of the CD4 gene in IPB-D2 chicken, i.e., g.7526 C>T, g.7825 C>T, g.8100 C>A, and g.8157 T>A. All CD4 SNPSs showed no association with IgY concentration and ND antibody titers. Relative mRNA expression shows that IPB-D2 chickens with high ND antibody titers have a higher level of expression when compared to IPB-D2 chickens with low ND antibody titers. Furthermore, pathway analysis showed the CD4 gene involved in the T cell receptor (TcR) signaling process. This study concludes that the CD4 gene is polymorphic and involved in the T cell receptor signaling process. This study demonstrated that polymorphisms of the CD4 gene in IPB-D2 chicken might not contribute to the IgY concentration and ND antibody titer but can serve as a reference in the study of CD4 genes in the other chicken breeds related to the other immunocompetence index.
Low-Cost W-Band Dual-Mode SIW Bandpass Filters Using Commercially Available Printed-Circuit-Board Technology

W-band filters play a crucial role in modern millimeter-wave communication and radar systems, finding applications in 5G/6G networks, automotive radar, and passive imaging. With the growing demand for higher data rates and efficient wireless communication, W-band filter development has gained significant attention in recent years. Various design techniques and fabrication technologies have been explored for improved performance and integration possibilities. This paper presents a planar substrate-integrated waveguide (SIW) dual-mode cavity filter as a solution to the challenges faced by previous designs in terms of integration, size, and cost. The dual-mode cavity coupling principle and design parameters are analyzed to optimize the filter’s performance. A W-band 4th-order dual-mode filter prototype is designed and fabricated on a 0.127-mm-thick RO5880 substrate, and a finline waveguide-to-microstrip transition structure is employed for compatibility with the test instrument. Simulation and experimental results demonstrate that the proposed filter exhibits low insertion loss, good in-band standing wave performance, and improved out-of-band suppression. Moreover, a visible high-frequency out-of-band transmission zero is implemented to enhance the steep roll-off characteristics in the high-frequency out-of-band region.
Therapeutic Aspects of Prunus cerasus Extract in a Rabbit Model of Atherosclerosis-Associated Diastolic Dysfunction

This study evaluates the potential therapeutic effects of anthocyanin-rich Prunus cerasus (sour cherry) extract (PCE) on atherosclerosis-associated cardiac dysfunction, described by the impairment of the NO-PKG (nitric oxide–protein kinase G) pathway and the antioxidant capacity. Initially, a rabbit model of atherosclerotic cardiovascular disease was established by administering a cholesterol-rich diet, enabling the examination of the impact of 9 g/kg PCE on the pre-existing compromised cardiovascular condition. After that, the animals were divided into four groups for 12 weeks: the (1) untreated control group; (2) PCE-administered healthy rabbits; (3) hypercholesterolemic (HC) group kept on an atherogenic diet; and (4) PCE-treated HC group. Dyslipidemia, impaired endothelial function, and signs of diastolic dysfunction were evident in hypercholesterolemic rabbits, accompanied by a reduced cardiac expression of eNOS (endothelial nitric oxide synthase), PKG, and SERCA2a (sarco/endoplasmic reticulum calcium ATPase 2a). Subsequent PCE treatment improved the lipid profile and the cardiac function. Additionally, PCE administration was associated with elevated myocardial levels of eNOS, PKG, and SERCA2a, while no significant changes in the vascular status were observed. Western blot analysis further revealed hypercholesterolemia-induced increase and PCE-associated reduction in heme oxygenase-1 expression. The observed effects of anthocyanins indicate their potential as a valuable addition to the treatment regimen for atherosclerosis-associated cardiac dysfunction.
Ensemble Model Based on Hybrid Deep Learning for Intrusion Detection in Smart Grid Networks

The Smart Grid aims to enhance the electric grid’s reliability, safety, and efficiency by utilizing digital information and control technologies. Real-time analysis and state estimation methods are crucial for ensuring proper control implementation. However, the reliance of Smart Grid systems on communication networks makes them vulnerable to cyberattacks, posing a significant risk to grid reliability. To mitigate such threats, efficient intrusion detection and prevention systems are essential. This paper proposes a hybrid deep-learning approach to detect distributed denial-of-service attacks on the Smart Grid’s communication infrastructure. Our method combines the convolutional neural network and recurrent gated unit algorithms. Two datasets were employed: The Intrusion Detection System dataset from the Canadian Institute for Cybersecurity and a custom dataset generated using the Omnet++ simulator. We also developed a real-time monitoring Kafka-based dashboard to facilitate attack surveillance and resilience. Experimental and simulation results demonstrate that our proposed approach achieves a high accuracy rate of 99.86%.
Low-Complexity DFT-Based Multibeamforming Arrays with Configurable Beam Profiles

A low-complexity multibeamforming scheme for uniform linear arrays is proposed based on warped and approximated discrete Fourier transform (DFT), albeit in the spatial domain. Conventional DFT- and approximated-DFT-based multibeamformers provide a low-complexity alternatives to produce multiple simultaneous beams with fixed beam profiles. The proposed approach aims to produce configurable (i.e., steerable) multibeam profiles using the warped DFT. Design examples are presented for an 8-element uniform linear array verifying, 81% and 18 % reduction in multiplier and adder complexities, respectively, when compared to a conventional narrowband multibeamformer having dedicated steering weights for each beam. Three system configurations are proposed using approximations for warped transform and the DFT. Hardware designs implemented on Xilinx Ultrascale KCU105 FPGA device verify 5.2 ns best case critical path delay.
A Data-Driven LSTM Soft Sensor Model Based on Bayesian Optimization for Hydraulic Pressure Measurement of Excavator

With the development of hardware and software, the pressure control of construction machinery embraced new characteristics of sensors’ failure. In recent years, soft sensors have been widely used in construction machinery for fault-tolerant controlling. To cope with the failure of some pressure sensors which cause great harm to the electro-hydraulic proportional control of excavators and accurately measure hydraulic pressure without actual pressure sensors, a pressure soft sensor based on long short-term memory (LSTM) neural network is proposed in this work. And Bayesian optimization (BO) is used to tune the hyperparameters of LSTM. The hydraulic system of an SY375IDS excavator is analyzed, and the mutual information (MI) strategy is adopted to determine features that are given to LSTM. In the case of multiple sensors’ failure at the same time, a multiple-input multiple-output (MIMO) prediction mechanism based on BO-LSTM and an MIMO cyclic prediction mechanism based on two multiple-input single-output (MISO) models are proposed. Finally, the performance of the proposed pressure soft sensor is evaluated in the compared experiments with back propagation neural network (BPNN), convolutional neural network (CNN), and recurrent neural network (RNN). The results show that the proposed method is superior to other methods in terms of pressure prediction.
The elderly population are more vulnerable for the management of colorectal cancer during the COVID-19 pandemic: a nationwide, population-based study

Background/Aims The impact of coronavirus disease 2019 (COVID-19) on the management of colorectal cancer (CRC) may worse in elderly population, as almost all COVID-19 deaths occurred in the elderly patients. This study aimed to evaluate the impact of COVID-19 on CRC management in the elderly population. Methods The numbers of patients who underwent colonoscopy, who visited hospitals or operated for CRC in 2020 and 2021 (COVID-19 era) were compared with those in 2019, according to 3 age groups (≥70 years, 50–69 years, and ≤49 years), based on the nationwide, population-based database (2019–2021) in South Korea. Results The annual volumes of colonoscopy and hospital visits for CRC in 2020 were more significantly declined in the old age group than in the young age group (both P<0.001). In addition, the annual volume of patients operated for CRC numerically more declined in old age group than in young age group. During the first surge of COVID-19 (March and April 2020), old age patients showed statistically significant declines for the monthly number of colonoscopies (–46.5% vs. –39.3%, P<0.001), hospital visits (–15.4% vs. –7.9%, P<0.001), CRC operations (–33.8% vs. –0.7%, P<0.05), and colonoscopic polypectomies (–41.8% vs. –38.0%, P<0.001) than young age patients, compared with those of same months in 2019. Conclusions Elderly population are more vulnerable for the management of CRC during the COVID-19 pandemic. Therefore, the elderly population are more carefully cared for in the management of CRC during the next pandemic.